5 ways to measure and improve your QA Effectiveness; is your vendor up to the mark?

The benefits of QA testing in software are widely accepted.  However, quantifying these benefits and optimizing the performance is tricky.  The performance of software development can be measured by the difficulty and amount of code committed in a given sprint.  Measuring the effectiveness of QA is harder when its success is measured by the lack of problems in software application deployment to production.

If you can’t measure it, you can’t improve it.

The ‘right’ metrics to evaluate QA effectiveness depend on your organization. However, it is generally a good idea to measure efficiency and performance for a well-rounded guide for performance evaluations.

Test coverage

While improving test coverage ideally means creating more tests and running them more frequently, this isn’t the actual goal, per se.  It will just mean more work if the right things are not getting tested with the right kind of test. Hence the total number of tests in your test suite by itself isn’t a good metric or reflection of your test coverage. 

Instead, a good metric to consider would be to check if your testing efforts cover 100% of all critical user paths.  The focus should be on building and maintaining tests to cover the most critical user flows of your applications.  You can check your analytics platform like Google Analytics or Amplitude to prioritize your test coverage.

Test reliability

The perfect test suite would have the correct correlation between failed tests and the number of defects identified.  A failed test will always include a real bug and the tests would only pass when the software is free of these bugs. 

The reliability of your test suite can be measured by comparing your results with these standards.  How often does your test fail due to problems with the test instead of actual bugs? Does your test suite have tests that pass sometimes and fail at other times for no identifiable reason?

Keeping track of why the tests fail over time, whether due to poorly-written tests, failures in the test environment, or something else, will help you identify the areas to improve.

Time to test

The time taken to test is a crucial indicator of how quickly your QA team creates and runs tests for the new features without affecting their quality. The tools that you use are a key factor here. This is where automated testing gains importance.

Scope of automation

Automated testing is faster than manual testing.  So one of the critical factors to measure your QA effectiveness would include the scope of automation in your test cycles.  What portion of your test cycle can be profitably automated, and how will it impact the time to run a test?  How many tests can you run in parallel, and the number of features that can be tested simultaneously to save time?

Time to fix

This includes the time taken to figure out whether a test failure represents a real bug or if the problem is with the test. It also includes the time taken to fix the bug or the test.  It is ideal to track each of these metrics separately so that you know which area takes the most time.

Escaped bugs

Tracking the number of bugs found after production release is one of the best metrics for evaluating your QA program. If customers aren’t reporting bugs, it is a good indication that your QA efforts are working.  When customers report bugs, it will help you identify ways to improve your testing.

If the bug is critical enough in the first two cases, the solution is to add a test or fix the existing test so your team can rely on it.  For the third case, you may need to look at how your test is designed—and consider using a tool that more reliably catches those bugs.

Is your Vendor up to the mark?

Outsourcing QA has become the norm on account of its ability to address the scalability of testing initiatives and bring in a sharper focus on outcome-based engagements.

Periodic evaluation of your QA vendor is one of the first steps to ensuring a rewarding long-term outsourcing engagement. Here are vital factors that you need to consider. 

Communication and people enablement

Clear and effective communication is an integral component of QA, more so when DevOps, Agile, and similar collaboration-heavy initiatives are pursued to achieve QA at scale. Ensure that there is effective communication right from the beginning of the sprint so that cross-functional teams are cognizant of the expectations from each of them and have their eye firmly fixed on the end goal of application release.

Also, your vendor’s ability to flex up/down to meet additional capacity needs is a vital factor for successful engagement. An assessment of the knowledge index of their team in terms of ability to learn your business and their ability to build fungibility (cross skill / multi-skill) into the team can help you evaluate their performance. 

Process Governance 

The right QA partner will be able to create a robust process and governing mechanism to track and manage all areas of quality and release readiness, visibility across all stages of the pipeline through reporting of essential KPIs, documentation for managing version control, resource management, and capacity planning. 

Vendor effectiveness can also be measured by their ability to manage operations and demand inflow. For example, at times, toolset disparity between various stages and multiple teams driving parallel work streams creates numerous information silos leading to fragmented visibility at the product level. The right process would focus on integration aspects as well to bridge these gaps.

Testing Quality 

The intent of a  QA process is mainly to bring down the defects between builds over the course of a project. Even though the total count of defects in a project may depend on different factors, measuring the rate of decline in the defects over time can help you understand how efficiently QA teams are addressing the defects. 

The calculation can be done by plotting the number of defects for each build and measuring the slope of the resulting line. A critical exception is when a new feature is introduced. This may increase the number of defects found in the builds. These defects should steadily decrease over time until the build becomes stable

Test Automation 

Measuring the time efficiency often boils down to the duration it takes to accomplish the task. While it takes a while to execute a test for the first time, subsequent executions will be much smoother and test times will reduce. 

You can determine the efficiency of your QA team by measuring the average time it takes to execute each test in a given cycle. These times should decrease after initial testing and eventually plateau at a base level. QA teams can improve these numbers by looking at what tests can be run concurrently or automated.

Improve your QA effectiveness with Trigent

Trigent’s experienced and versatile Quality Assurance and the Testing team is a major contributor to the successful launch, upgrade, and maintenance of quality software used by millions around the globe. Our experienced responsible testing practices put process before convenience to delight stakeholders with an impressive industry rivaled Defect Escape Ratio or DER of 0.2.

Trigent is an early pioneer in IT outsourcing and offshore software development business. We enable organizations to adopt digital processes and customer engagement models to achieve outstanding results and end-user experience. We help clients achieve this through enterprise-wide digital transformation, modernization, and optimization of their IT environment. Our decades of experience, deep domain knowledge, and technology expertise deliver transformational solutions to ISVs, enterprises, and SMBs.

Ensure the QA effectiveness and application performance. Talk to us

Quality Assurance outsourcing in the World of DevOps-Best Practices for Dispersed (Distributed) Quality Assurance Team

Why Quality Assurance (QA) outsourcing is good for business

The software testing services is expected to grow by more than USD 55 Billion between 2022-2026. With outsourced QA being expedited through teams distributed across geographies and locations, many aspects that were hitherto guaranteed through co-located teams have now come under a lot of pressure. Concerns range from constant communication and interworking to coverage across a wide range of testing types – unit testing, API testing, as well as validating experiences across a wide range of channels.

Additionally, it is essential to note that DevOps is the preferred methodology for software development and release, with collaborating teams oriented towards faster delivery cycles augmented by early feedback. And QA is regarded as a critical binding thread of DevOps practice, thereby ensuring a balanced approach in maintaining collaboration and communication between teams while ensuring that delivery cycles are up to speed and the quality of the delivered product meets customer expectations.

Best practices for ensuring the effectiveness of distributed QA teams

Focus on the right capability: 
While organizations focus to a large extent on bringing capabilities across development, support, QA, operations, and product management in a scrum team, paramount from a quality perspective would be QA skills. The challenge is to find the right skill mix. For example, a good exploratory tester; and automation skills (not necessarily in the same person). In addition, specialist skills related to performance, security, and accessibility also need to be thought through. The key is to choose an optimum mix of specialists and generalists.

Aim to achieve the right platform/tool mix: 
It is vital to maintain consistency across the tool stacks used for engagement. According to a 451 research survey, 39% of respondents juggle between 11 to 30 tools to keep an eye on their application infrastructure and cloud environment; 8% are found to use over 21 to 30 tools. Commonly referred to as tool sprawl, this makes it extremely difficult to collaborate in an often decentralized and distributed QA environment. It’s imperative to have a balanced approach toward the tool mix by influencing the team to adopt a common set of tools instead of making it mandatory.

Ensure a robust CI/CD process and environment:
A weak and insipid process may cause the development and operations team to run into problems while integrating new code.  With several geographically distributed teams committing code consistently into the CI environment, shared dev/test/deploy environments constantly run into issues if sufficient thought process has not gone into identifying environment configurations.  These can ultimately translate into failed tests and, thereby, failed delivery/deployment.  A well-defined automated process ensures continuous deployment and monitoring throughout the lifecycle of an application, from integration and testing phases through to the release and support.

A good practice would be to adopt cloud-based infrastructure, reinforced by mechanisms for managing any escalations on deployment issues effectively and quickly.  Issues like build failure or lack of infrastructure support can hamper the productivity of distributed teams.  When strengthened by remote alerts, robust reporting capabilities for teams, and resilient communication infrastructure, accelerated development to deployment becomes a reality.

Follow good development practices:
Joint backlog grooming exercises with all stakeholders, regular updates on progress, code analysis, and effective build and deployment practices, as well as establishing a workflow for defect/issue management, are paramount in ensuring the effectiveness of distributed teams. Equally important is the need to manage risk early with ongoing impact analysis, code quality reviews, risk-based testing, and real-time risk assessments. In short, the adoption of risk and impact assessment mechanisms is vital.

Another critical area of focus is the need to ascertain robust metrics that help in the early identification of quality issues and ease the process of integration with the development cycle. Research conducted in 2020 by Gatepoint and Perfecto surveyed executives from over 100 leading digital enterprises in the United States on their testing habits, tools, and challenges. The survey results showed that 63 percent start testing only after a new build and code is developed. Just 40 percent test upon each code change or at the start of new software.

Devote special attention to automation testing:
Manual (or exploratory) testing allows you to ensure that product features are well tested, while automation of tests (or, as some say, checks) helps you improve coverage for repeatable tasks. Though planning for both during your early sprint planning meetings is essential, test automation services have become an integral testing component. 

As per studies, in 2020, approximately 44 percent of IT companies have automated half of their testing. Businesses are continuously adopting test automation to fulfill the demand for quality at speed. Hence it is no surprise that according to Data Bridge Market research, the automation testing market will reach an estimated value of USD 19.9 billion by 2028 and grow at a CAGR of 14.89% in the forecast period of 2021 to 2028.

Outsourcing test automation is a sure-shot way of conducting testing and maintaining product quality. Keeping the rising demand in mind, let us look at a few benefits of outsourcing test automation services.

Early non-functional focus: 
Organizations tend to overlook the importance of bringing in occasional validations of how the product fares around performance, security vulnerabilities, or even important regulations like accessibility until late in the day. As per the 2020 DevSecOps Community Survey, 55 percent of respondents deploy at least once per week, and 11 percent claim multiple daily deployments.  But when it comes to security, 44 percent of the mature DevOps practices know it’s important but don’t have time to devote to it.

Security has a further impact on CI/CD tool stack deployment itself, as indicated by a 451 research in which more than 60% of respondents said a lack of automated, integrated security tools is a big challenge in implementing CI/CD tools effectively. 

It is essential that any issue which is non-functional in nature be exposed and dealt with before it moves down the dev pipeline. Adoption of a non-functional focus depends to a large extent on the evolution of the product and the risk to the organization.

Benefits of outsourcing your QA

To make distributed QA teams successful, an organization must have the capability to focus in a balanced and consistent way across the length and breadth of the QA spectrum, from people and processes to technology stacks. However, the ability to make these practices work hinges on the diligence with which an organization institutionalizes these best practices as well as platforms and supplements it with an organizational culture that is open to change.

Trigent’s experienced and versatile Quality Assurance and Testing team is a major contributor to the successful launch, upgrade, and maintenance of quality software used by millions around the globe. Our experienced, continuous testing practices put process before convenience to delight stakeholders with an impressive industry rivaled Defect Escape Ratio or DER of 0.2.

Ensure increased application availability and infrastructure performance. Talk to us.

5 Ways You are Missing Out on ROI in QA Automation

QA Automation is for everyone, whether you are a startup with a single product and few early adopters or a mid-sized company with a portfolio of products and multiple deployments. It assures product quality by ensuring maximum test coverage that is consistently executed prior to every release and is done in the most efficient manner possible.

Test Automation does not mean having fewer Test Engineers – It means using them efficiently in scenarios that warrant skilled testing, with routine and repetitive tests automated.

When done right, Test Automation unlocks significant value for the business. The Return on Investment (RoI) is a classical approach that attempts to quantify the impact and in turn, justify the investment decision.

However, the simplistic approach that is typically adopted to compute RoI provides a myopic view of the value derived from test automation. More importantly, it offers very little information to the Management on how to leverage additional savings and value from the initiative. Hence, it is vital that the RoI calculations take into account all the factors that contribute to its success.

Limitations of the conventional model to compute Test Automation ROI

Software leadership teams treat QA as a cost center and therefore apply a simplistic approach to computing RoI. The formula applied is

You may quickly notice the limitation in this formula. RoI should take into account the ‘Returns’ gained from ‘Investments’ made.

By only considering the Cost Savings gained from the reduction in testing, the true value of Test Automation is grossly underestimated.

In addition to the savings in terms of resources, attributes like the value of faster time to market, the opportunity cost of a bad Customer Experience due to buggy code, and being resilient to attrition, need to be factored in to fully compute the “Returns” earned. How to determine the value of these factors and incorporate them into the RoI formula is another blog in itself

Beyond Faster Testing – 5 ways to lower costs with Test Automation

For the moment, we will explore how companies can derive maximum savings while in Test Automation implementation. While calculating the ‘Cost Savings’ component of the RoI, it is important to take at least a 3-year view of the evolution of the product portfolio and its impact on the testing needs. The primary reason is that the ratio of manual tests to regression tests decreases over time and the percentage of tests that can be automated to total tests increases. With this critical factor in mind, let us look at how businesses can unlock additional savings

Test Automation Framework – Build vs. Partner

The initial instinct of software teams is to pick one of the open-source frameworks and quickly customize it for your specific need. While it’s a good strategy to get started, as the product mix grows and the scope of testing increases, considerable effort is needed to keep the Framework relevant or to fully integrate the framework into your CI/CD pipeline. This additional effort could wipe away any gains made with test automation

By using a vendor or testing partner’s Test Automation Framework, the Engineering team can be assured that it’s versatile to suit their future needs, give them the freedom to use different tools, and most importantly benefit from the industry best practices, thereby eliminating trial and error.

Create test scripts faster with ‘Accelerators’

When partnering with a QE provider with relevant domain expertise, you can take advantage of the partners’ suite of pre-built test cases to get started quickly. With little or no customization, the ‘accelerators’ allow you to create and run your initial test scripts and get results faster.

Accelerators also serve as a guide to design a test suite that maximizes coverage

Using accelerators to create the standard use cases typical for that industry ensures that your team has the bandwidth to invest in the use cases unique to your product and requires special attention.

Automate Test Design, Execution and Maintenance

When people talk of Test Automation, the term “automate” usually refers to test execution. However, execution is just 30% of the testing process. To accelerate the pace of production releases require unlocking efficiency across the testing cycle including design & maintenance.

Visual Test Design to gather functional requirements and develop the optimal number of most relevant tests, AI tools for efficient and automated test maintenance without generating any technical debt need to be leveraged. When implemented right, they deliver 30% gains in creation and 50% savings in maintenance

Shift Performance Testing left with Automation

In addition to creating capacity for the QA team to focus on tests to assure that the innovations deliver the expected value, you can set up Automated Performance Testing to rapidly check the speed, response time, reliability, resource usage, and scalability of software under an expected workload.

Shifting performance testing left allows you to identify potential performance bottleneck issues earlier in the development cycle. Performance issues are tricky to resolve, especially if issues are related to code or architecture. Test Automation enables automated performance testing and in turn, assures functional and performance quality.

Automate deployment of Test Data Sets

Creating or generating quality test data, specially Transactional Data Sets, have been known to cause delays. Based on our experience, the average time lost in waiting for the right test data is 5 days, while for innovation use cases, they take weeks. For thorough testing, often the test data needs to change during the execution of the test, which needs to be catered for

With a Test Data Automation, the test database can be refreshed on-demand. Testers access data subsets required for their suite of test cases and consistent data sets are utilized across multiple environments. Using a cogent test data set across varied use cases allows for data-driven insights for the entire product – which would be difficult with test data silos

Maximize your ROI with Trigent

The benefits, and therefore the ‘Returns’, from Test Automation, go well beyond the savings from reduced manual testing time and effort. It also serves as insurance against attrition! Losing people is inevitable, but you can ensure that the historical product knowledge is retained with your extensive suite of automated test scripts.

Partnering with a QE Service Provider having relevant domain experience will enable you to get your Quality processes right the first time – And get it done fast. Saving you valuable time and money. And it frees up your in-house team to focus on the test cases to assure the customer experiences that make your product special.

Do your QA efforts meet all your application needs? Is it yielding the desired ROI? Let’s talk!

Five Metrics to Track the Performance of Your Quality Assurance Teams and the efficiency of your Quality Assurance strategy

Why Quality Assurance and Engineering?

A product goes through different stages of a release cycle, from development and testing to deployment, use, and constant evolution. Organizations often seek to hasten their long release cycle while maintaining product quality. Additionally, ensuring a superior and connected customer experience is one of the primary objectives for organizations. According to a PWC research report published in 2020, 1 in 3 customers is willing to leave a brand after one bad experience. This is where Quality Engineering comes in.

There is a need to swiftly identify risks, be it bugs, errors, and problems, that can impact the business or ruin the customer experience. Most of the time, organizations cannot cover the entire scope of their testing needs, and this is where they decide to invest in Quality Assurance outsourcing.

Developing a sound Quality Assurance (QA) strategy

Software products are currently being developed for a unified CX. To meet the ever-evolving customer expectations, applications are created to deliver a seamless experience on multiple devices on various platforms. Continuous testing across devices and browsers, as well as apt deployment of multi-platform products, are essential. These require domain expertise, complimenting infrastructure, and a sound QA strategy. According to a report published in 2020-2021, the budget proportion allocated for QA was approximately 22%. 

Digital transformation has a massive impact on the time-to-market. Reduced cycle time for releasing multiple application versions by adopting Agile and DevOps principles has become imperative for providing a competitive edge. This has made automation an irreplaceable element in one’s QA strategy. With automation, a team can run tests for 16 additional hours (excluding the 8 hours of effort, on average, by a manual tester) a day, thus reducing the average cost of testing hours. In fact, as per studies, in 2020, approximately 44 percent of IT companies have automated half of their testing. 

A thorough strategy provides transparency on delivery timelines and strong interactions between developers and the testing team that comprehensively covers every aspect of the testing pyramid, from robust unit tests and contracts to functional end-to-end tests. 

Key performance metrics for QA

There are a lot of benefits to tracking performance metrics. QA performance metrics are essential for discarding inefficient strategies. The metrics also enable managers to track the progress of the QA team over time and make data-driven decisions. 

Here are five metrics to track the performance of your Quality Assurance team and the efficiency of your Quality Assurance strategy. 

1) Reduced risk build-on-build:

This metric is instrumental in ensuring a build’s stability over time by revealing the valid defects in builds. The goal is to decrease the number of risks impacting defects from one build compared to the next over the course of the QA project. However, this strategy, whilst keeping risk at the center of any release, aims to achieve the right levels of coverage across new and existing functionality. 

If the QA team experiences a constant increase in risk impacting defects, it may be because of the following reasons:

To measure the effectiveness further, one should also note the mean time to detect and the mean time to repair a defect.

2) Automated tests

Automation is instrumental in speeding up your release cycle while maintaining quality as it increases the depth, accuracy, and, more importantly, coverage of the test cases. According to a research report published in 2002, the earlier a defect is found, the more economical it is to fix, as it costs approximately five times more to fix a coding defect once the system is released.

With higher test coverage, an organization can find more defects before a release goes into production. Automation also significantly reduces the time to market by expediting the pace of development and testing. In fact, as per a 2020-2021 survey report, approximately 69% of the survey respondents stated reduced test cycle time to be a key benefit of automation. 

To ensure that the QA team maintains productivity and efficiency levels, measuring the number of automation test cases and delivering new automation scripts is essential. The metric monitors the speed of test case delivery and identifies the programs needing further testing. We recommend analyzing your automation coverage by monitoring total test cases. 

While measuring this metric, we recommend taking into account:

  • Requirements coverage vs. automated test coverage
  • Increased test coverage due to automation (for instance, multiple devices/browsers)
  • Total test duration savings

3) Tracking the escaped bugs and classifying the severity of bugs:

Ideally, there should be no defects deployed into the production. However, despite best efforts, most of the time, bugs make it into production. To track this would involve the team establishing checks and balances and classifying the severity of the defects. The team can measure the overall impact by analyzing the bugs of high severity levels that made into production. This is one of the best overall metrics for evaluating the effectiveness of your QA processes.  Customer-reported issues/defects may help identify specific ways to improve testing. 

4) Analyzing the execution time of test cycles:

The QA teams should keep track of the time taken to execute a test. The primary aim of this metric is to record and verify the time taken to run a test for the first time compared to subsequent executions. This metric can be a useful one to identify automation candidates, thereby reducing the overall test cycle time. The team should identify tests that can be run concurrently to increase effectiveness. 

5) Summary of active defects

This includes a team capturing information such as the names and descriptions of a defect. The team should keep a track/summary of verified, closed, and reopened defects over time. A low trajectory in the number of defects indicates a high quality of a product.

Be Agile and surge ahead in your business with Trigent’s QE services 

Quality Assurance is essential in every product development, and applying the right QA metrics enables you to track your progress over time. Trigent’s quality engineering services empower organizations to increase customer adoption and reduce maintenance costs by delivering a superior-quality product that is release-ready.

Are you looking to build a sound Quality Assurance strategy for your organization? Need Help? Talk to us. 

5 ways QA can help you accelerate and improve your DevOps CI/CD cycle

A practical and thorough testing strategy is essential to keep your evolving application up to date with industry standards.

In today’s digital world, nearly 50% of organizations have automated their software release to production. It is not surprising given that 80% of organizations prioritize their CX and cannot afford a longer wait time to add new features to their applications.  A reliable high-frequency deployment can be implemented by automating the testing and delivery process. This will reduce the total deployment time drastically. 

Over 62% of enterprises use CI/CD (continuous integration/continuous delivery) pipelines to automate their software delivery process.  Yet once the organization establishes its main pipelines to orchestrate software testing and promotion, these are often left unreviewed.  As a result, the software developed through the CI/CD toolchains evolve frequently.  While the software release processes remain stagnant. 

The importance of an optimal QA DevOps strategy

DevOps has many benefits in reducing cost, facilitating scalability, and improving productivity. However, one of its most critical goals is to make continuous code deliveries faster and more testable. This is achieved by improving the deployment frequency with judicious automation both in terms of delivery and testing. 

Most successful companies deploy their software multiple times a day. Netflix leverages automation and open source to help its engineers deploy code thousands of times daily. Within a year of its migration to AWS, Amazon engineers’ deployed code every 11.7 seconds with robust testing automation and deployment suite.  

A stringent automated testing suite is essential to ensure system stability and flawless delivery. It helps ensure that nothing is broken every time a new deployment is made. 

The incident of Knight Capital underlines this importance. For years, Knight relied on an internal application named SMARS to manage their buy orders in the stock market. This app had many outdated sections in its codebase that were not removed. While integrating a new code, Knight overlooked a bug that inadvertently called one of these obsolete features. This resulted in the company making buy orders worth billions in minutes. It ended up paying a $460M fine and going bankrupt overnight.

A good QA protects against the failed changes and ensures that it does not trickle down and affects the other components.  Implementing test automation in CI/CD will ensure that every new feature undergoes unit, integration, and functional tests. With this, we can have a highly reliable continuous integration process with greater deployment frequency, security, reliability, and ease. 

An optimal QA strategy to streamline the DevOps cycle would include a well-thought-out and judiciously implemented automation for QA and delivery. This would help in ensuring a shorter CI/CD cycle. It would also offer application stability and recover from any test failure without creating outages. Smaller deployment packages will ensure easier testing and faster deployment. 

5 QA testing strategies to accelerate CI/CD cycle

Most good DevOps implementations include strong interactions between developers and rigorous, in-built testing that comprehensively covers every level of the testing pyramid. This includes robust unit tests and contracts for API and functional end-to-end tests. 

Here are 5 best QA testing strategies you should consider to improve the quality of your software release cycles:

Validate API performance with API testing

APIs are one of the most critical components of a software application. It holds together the different systems involved in the application. The different entities that rely on the API, ranging from users, mobile devices, IoT devices, and applications, are also constantly expanding. Hence it is crucial to test and ensure its performance. 

Many popular tools such as Soap UI and Swagger can easily be plugged into any CI/CD pipeline. These tools help execute API tests directly from your pipeline. This will help you build and automate your test suites to run in parallel and reduce the test execution time.

Ensure flawless user experience with Automated GUI testing

Just like an API, the functionality and stability of GUIs are critical for a successful application rollout.  GUI issues after production rollout can be disastrous users wouldn’t be able to access the app or parts of its functionality.  Such issues would be challenging to troubleshoot as they might reside in individual browsers or environments. 

A robust and automated GUI test suite covering all supported browsers and mobile platforms can shorten testing cycles and ensure a consistent user experience. Automated GUI testing tools can simulate user behavior on the application and compare the expected results to the actual results. GUI testing tools like Appium and Selenium help testers simulate the user journey.  These testing tools can be integrated with any CI/CD pipeline. 

Incorporating these tools in your automated release cycle can validate GUI functions across various browsers and platforms.

Handle unscheduled outages with Non-functional testing

You may often encounter unexpected outages or failures once an application is in production. These may include environmental triggers like a data center network interruption or unusual traffic spikes. These are often outlying situations that may lead to a crisis, provided your application cannot handle it with grace. Here lies the importance of automated non-functional testing

Nonfunctional testing incorporates an application’s behavior under external or often uncontrollable factors, such as stress, load, volume, or unexpected environmental events. It is a broad category with several tools that can be incorporated into the CI/CD cycle. Integrating automated non-functional testing gates within your pipeline is advisable before the application gets released to production.

Improve application security with App Sec testing

Many enterprises don’t address security until later in the application release cycle. The introduction of DevSecOps has increased focus on including security checkpoints throughout the application release lifecycle. The earlier a security vulnerability is identified, the cheaper it is to resolve. Today, different automated security scanning tools are available depending on the assets tested.

The more comprehensive your approach to security scanning, your organization’s overall security posture will be better. Introducing checkpoints early is often a great way to impact the quality of the released software. 

Secure end-to-end functionality with Regression testing 

Changes to one component may sometimes have downstream effects across the complete system functionality. Since software involves many interconnected parts today, it’s essential to establish a solid regression testing strategy.

Regression testing should verify that the existing business functionality performs as expected even when changes are made to the system. Without this, bugs and vulnerabilities may appear in the system components. These problems become harder to identify and diagnose once the application is released. Teams doing troubleshooting may not know where to begin, especially if the release did not modify the failing component.

Accelerate your application rollout with Trigent’s QA services

HP LaserJet Firmware division improved its software delivery process and reduced its overall development cost by 40%. They achieved this by implementing a delivery process that focussed on test automation and continuous integration. 

Around 88% of organizations that participated in research conducted on CI/CD claim they lack the technical skill and knowledge to adopt testing and deployment automation. The right QA partner can help you devise a robust test automation strategy to reduce deployment time and cost. 

New-age applications are complex. While the DevOps CI/CD cycle may quicken its rollout, it may fail if not bolstered by a robust QA strategy. QA is integral to the DevOps process; without it, continuous development and delivery are inconceivable. 

Does your QA meet all your application needs? Need help? Let’s talk

Continuously Engineering Application Performance

The success of an application today hinges on customer experience. To a large extent, it’s the sum of two components, one being the applicability of the software product features to the target audience and the second, the experience of the customer while using the application. In October 2021, a six-hour outage of the Facebook family of apps cost the company nearly $100 million in revenue. Instances like these underline the need to focus on application performance for a good customer experience. We are witnessing an era of zero patience, making application speed, availability, reliability, and stability more paramount to product release success. 

Modern application development cycles are agile, or DevOps led, effectively addressing application functionality through MVP and subsequent releases. However, the showstopper in many cases is application underperformance. This is an outcome of the inability of an organization to spend enough time analyzing release performance in real-life scenarios. Even in agile teams, performance testing happens one sprint behind other forms of testing. With an increase in the number of product releases, the number of times application performance checks can be done, and the window available to do full-fledged performance testing is reducing.

How do you engineer for performance?

Introducing performance checks & testing early in the application development lifecycle helps to detect issues, identify potential performance bottlenecks early on and take corrective measures before they have a chance to compound over subsequent application releases. This also brings to the fore predictive performance engineering – the ability to foresee and provide timely advice on vulnerable areas. By focusing on areas outlined in the subsequent sections, organizations can move towards continuously engineering applications for superior performance rather than a testing application for superior performance.

Adopt a performance mindset focused on risk and impact

Adopting a performance mindset the moment a release is planned can help anticipate many common performance issues. The risks applicable to these issues can be classified based on various parameters like scalability, capacity, efficiency, resilience, etc. The next step is to ascertain the impact those risks can have on the application performance, which can further be used to stack rank the performance gaps and take remedial measures.

An equally important task is the choice of tools/platforms adopted in line with the mindset. For, e.g., evaluating automation capability for high scale load testing, bringing together insights on the client as well as server-side performance & troubleshooting, or carrying out performance testing with real as well as virtual devices, all the while mapping such tools against risk impact metrics.

Design with performance metrics in mind

Studies indicate that many performance issues remain unnoticed during the early stages of application development. With each passing release, they mount up before the application finally breaks down when it encounters a peak load. When that happens, there arises a mandate to revisit all previous releases from a performance point of view, which is a cumbersome task. Addressing this issue calls for a close look at behaviors that impact performance and building them into the design process.

·         Analyzing variations or deviations in past metrics from component tests,

·         Extending static code analysis to understand performance impacts/flaws, and

·      Dynamic code profiling to understand how the code performs during execution, thereby exposing runtime vulnerabilities.

Distribute performance tests across multiple stages

Nothing could be more error-prone than scheduling performance checks towards the end of the development lifecycle. When testing each build, it makes a lot more sense to incorporate performance-related checks as well. At the unit level, you can have a service component test for analyzing at an individual service level and a product test focusing on the entire release delivered by the team. Break testing individual components continuously through fast, repeatable performance tests will help to understand their tolerances and dependencies on other modules.

For either of the tests mentioned above, mocks need to be created early to ensure that interfaces to downstream services are taken care of, without dependency on those services to be up and running. This should be followed by assessing integration performance risk whereby code developed by multiple DevOps teams is brought together. Performance data across each build can be fed back to take corrective actions along the way. Continuously repeating runs of smaller tests and providing real-time feedback to the developers help them understand the code development much better and quickly make improvements to the code.

Evaluate application performance at each stage of the CI/CD pipeline

Automating and integrating performance testing into the CI/CD process involves unit performance testing at the code & build stages, integration performance testing when individual software units are integrated, system-level performance testing and load testing, and real user monitoring when the application moves into a production environment. Prior to going live, it would be good to test the performance of the complete release to get an end-to-end view.

Organizations that automate and integrate performance tests into the CI/CD process are a common practice that runs short tests as part of the CI cycle unattended. What is needed is the ability to monitor the test closely as it runs and look for anomalies or signs of failure that point to a corrective action to be taken on the environment or on the scripts as well as application code. Metrics from these tests can be compared to performance benchmarks created as part of the design stage. The extent of deviations from benchmarks can point to code-level design factors causing performance degradation.

Assess performance in a production environment

Continuous performance monitoring happens after the application goes live. The need at this stage is to monitor application performance through dashboards, alerts, etc., and compare those with past records and benchmarks. The analysis can then decode performance reports across stages to foresee risks and provide amplified feedback into the application design stage.

Another important activity that can be undertaken at this stage is to monitor end-user activity and sentiment for performance. The learnings can further be incorporated into the feedback loop driving changes to subsequent application releases.

Continuously engineer application performance with Trigent

Continuously engineering application performance plays a critical role in improving the apps’ scalability, reliability, and robustness before they are released into the market. With years of expertise in quality engineering, Trigent can help optimize your application capacity, address availability irrespective of business spikes and dips, and ensure first-time-right product launches and superior customer satisfaction and acceptance.

Does your QA meet all your application needs? Let’s connect and discuss

TestOps – Assuring application quality at scale

The importance of TestOps

Continuous development, integration, testing, and deployment have become the norm for modern application development cycles. With the increased adoption of DevOps principles to accelerate release velocity, testing has shifted left to be embedded in the earlier stages of the development process itself. In addition, microservices-led application architecture has led to the adoption of shift right testing and testing individual services, and releases in the later stages of development, adding further complexity to the way quality is assured.

These challenges underline the need for automated testing. An increasing number of releases on one hand and an equally reducing release cycle times on the other have led to a strong need to exponentially increase the number of automated tests developed sprint after sprint. Although automation test suites reduce testing times, scaling these suites for large application development cycles mandates a different approach.

TestOps for effective DevOps – QA integration

In its most simplistic definition, TestOps brings together development, operations, and QA teams and drives them to collaborate effectively to achieve true CI/CD discipline. Leveraging four core principles across planning, control, management, and insights helps achieve test automation at scale.

  • Planning helps the team prioritize key elements of the release and analyze risks affecting QA like goals, code complexity, test coverage, and automatability. It’s an ongoing collaborative process that embeds rapid iteration for incorporating faster feedback cycles into each release.
  • Control refers to the ability to perform continuous monitoring and adjust the flow of various processes. While a smaller team might work well with the right documentation, larger teams mandate the need for established processes. Control essentially gives test ownership to the larger product team itself regardless of what aspect of testing is being looked at like functional, regression, performance, or unit testing.
  • Management outlines the division of activities among team members, establishes conventions and communication guidelines, and organizes test cases into actionable modules within test suites. This is essential in complex application development frameworks involving hundreds of developers, where continuous communication becomes a challenge.
  • Insight is a crucial element that analyses data from testing and uses it to bring about changes that enhance application quality and team effectiveness. Of late, AI/ML technologies have found their way into this phase of TestOps for better QA insights and predictions.

What differentiates TestOps

Unlike existing common notions, TestOps is not merely an integration of testing and operations. The DevOps framework already incorporates testing and collaboration right from the early stages of the development cycle. However, services-based application architecture introduces a wide range of interception points that mandate testing. These, combined with a series of newer test techniques like API testing, visual testing, and load and performance testing, slow down release cycles considerably. TestOps complements DevOps to plan, manage and automate testing across the entire spectrum, right from functional and non-functional testing to security and CI/CD pipelines. TestOps brings the ability to continuously test multiple levels with multiple automation toolsets and manage effectively to address scale.

TestOps effectively integrates software testing skillset and DevOps capability along with an ability to create an automation framework with test analytics and advanced reporting. By managing test-related DevOps initiatives, it can effectively curate the test pipeline, own it, manage effectively to incorporate business changes, and adapt faster. Having visibility across the pipeline through automated reporting capabilities also brings the ability to detect failing tests faster, driving faster business responses.

By sharply focusing on test pipelines, TestOps enables automatic and timely balancing of test loads across multiple environments, thereby driving value creation irrespective of an increase in test demand. Leveraging actionable insights on test coverage, release readiness, and real-time analysis, TestOps ups the QA game through root cause analysis of application failure points, obviating any need to crunch tons of log files for relevant failure information.

Ensure quality at scale with TestOps

Many organizations fail to consistently ensure quality across their application releases in today’s digital-first application development mode. The major reason behind this is their inability to keep up with test coverage of frequent application releases. Smaller teams ensure complete test coverage by building appropriate automation stacks and effectively collaborating with development and operations teams. For larger teams, this means laying down automation processes, frameworks, and toolsets to manage and run test pipelines with in-depth visibility into test operations. For assuring quality at scale, TestOps is mandatory. 

Does your QA approach meet your project needs at scale? Let’s talk

QE strategy to mitigate inherent risks involved in application migration to the cloud

Cloud migration strategies, be it lift & shift, rearchitect or rebuild, are fraught with inherent risks which need to be eliminated with the right QE approach

The adoption of cloud environments has been expanding for several years and is presently in an accelerated mode. A multi-cloud strategy is the defacto approach adopted by multiple organizations, as per Flexera 2022 State of the Cloud Report. The move toward cloud-native application architectures, exponential scaling needs of applications, and increased frequency and speed of product release launches have contributed to increased cloud adoption.

The success of migrating the application landscape to the cloud hinges on the ability to perform end-to-end quality assurance initiatives specific to the cloud. 

Underestimation of application performance

Availability, scalability, reliability, and high response rates are critical expectations from an application in a cloud environment. Application performance issues can come to light on account of incorrect sizing of servers or network latency issues that might not have surfaced when the application is tested in isolation. It can also be an outcome of an incorrect understanding of probable workloads that can be managed by an application while in a cloud environment. 

The right performance engineering strategy involves designing for performance in mind and fulfilling performance validations, including load testing. This ensures that the application under test remains stable in normal and peak conditions and defines and sets up application monitoring toolsets and parameters. There needs to be an understanding of workloads with the potential to be moved to the cloud and ones that need to remain on-premise. Incompatible application architectures need to be identified. Load testing should be carried out in parallel to record SLA response times across various loads for those moved to the cloud. 

Security and compliance

With the increased adoption of data privacy norms like GDPR and CCPA, there is a renewed focus on ensuring the safety of data migrated from application to cloud. Incidents like the one with Marriott hotel, where half a million sensitive customer information like credit cards and identity were compromised, have brought the need to test the security of data loaded onto cloud environments. 

A must-have element of a sound QA strategy is to ensure that both applications and data are secure and can withstand malicious attacks. With cybersecurity attacks increasing both in quantity and innovative tactics, there is a strong need for the implementation of security policies and testing techniques, including but not limited to vulnerability scanning, penetration testing, and threat and risk assessment. These are aimed at the following.

  • Identifying security gaps and weaknesses in the system
  • DDoS attack prevention
  • Provide actionable insights on ways to eliminate potential vulnerabilities

Accuracy of Data migration 

Assuring the quality of data that is being migrated to the cloud remains the top challenge, without which the convenience and performance expectation from cloud adoption falls flat. It calls for assessing quality before migrating, monitoring during migration, and verifying the integrity and quality post-migration. This is fraught with multiple challenges like migrating from old data models, duplicate record management, and resolving data ownership, to name a few. 

White-box migration testing forms a key component of a robust data migration testing initiative. It starts off by logically verifying a migration script to guarantee it’s complete and accurate. This is followed by ensuring database compliance with required preconditions, e.g., detailed script description, source, and receiver structure, and data migration mapping. Furthermore, the QA team analyzes and assures the structure of the database, data storage formats, migration requirements, the formats of fields, etc. More recently, predictive data quality measures have also been adopted to get a centralized view and better control over data quality. 

Application Interoperability

Not all apps that need to migrate to the cloud may be compatible with the cloud environment. Some applications show better performance in a private or hybrid cloud than in a public cloud. Some others require minor tweaking, while others may require extensive reengineering or recoding. Not identifying cross-application dependencies before planning the migration waves can lead to failure. Equally important is the need to integrate with third-party tools for seamless communication across applications without glitches. 

A robust QA strategy needs to identify applications that are part of the network, their functionalities, and dependencies among applications, along with each app’s SLA since dependencies between systems and applications can make integration testing potentially challenging. Integration testing for cloud-based applications brings to the fore the need to consider the following: 

  • Resources for the validation of integration testing 
  • Assuring cloud migration by using third-party tools
  • Discovering glitches in coordination within the cloud
  • Application configuration in the cloud environment
  • Seamless integration across multiple surround applications

Ensure successful cloud migration with Trigent’s QE services

Application migration to the cloud can be a painful process without a robust QE strategy. With aspects such as data quality, security, app performance, and seamless connection with a host of surrounding applications being paramount in a cloud environment, the need for testing has become more critical than ever. 

Trigent’s cloud-first strategy enables organizations to leverage a customized, risk-mitigated cloud strategy and deployment model most suitable for the business. Our proven approach, frameworks, architectures, and partner ecosystem have helped businesses realize the potential of the cloud.

We provide a secure, seamless journey from in-house IT to a modern enterprise environment powered by Cloud. Our team of experts has enabled cloud transformation at scale and speed for small, medium, and large organizations across different industries. The transformation helps customers leverage the best architecture, application performance, infrastructure, and security without disrupting business continuity. 

Ensure a seamless cloud migration for your application. Contact us now!

Intelligent quality engineering (QE) in continuous integration and delivery

With digital adoption being on an accelerated path than ever before, faster launch to the market and continuous delivery have become a prerequisite for competitive differentiation. While CI/CD pipeline-based software development has become the norm, QE’s role in the CI/CD-based development process is equally important. Continuous integration increases the frequency of running software builds, thereby increasing the need to run all tests and translating into an exponential increase in time and resource intensity.

Ensuring a reliable release depends mainly on the ability to test early and often to address defects as soon as they are committed to the pipeline. While there is a steadfast focus on continuous testing in a CI pipeline before any new code gets committed to the existing codebase, the effort spent on identifying the right set of tests to run can benefit from more attention. An intelligent way of accomplishing this involves prioritizing test case creation based on what changed recently in the application build while avoiding tests that have already run on validated portions of the application under test.

This article aims to outline some of the ways of accomplishing this objective by incorporating Artificial Intelligence (AI) principles.

Intelligent prioritization for continuous integration and continuous delivery with QE

This involves identifying those tests that map to the changes in the new code build. The changes are evaluated to create newer test cases with a high chance of failure since they have not been tested before. By deprioritizing those test cases that have meager failure rates on account of being used widely in earlier build stages and prioritizing newer test cases based on build changes, time and effort are involved in assuring QA gets reduced. Using model-based testing techniques to create the required tests and then applying ML-based prioritization on those tests will help make continuous testing more efficient.

Read more: Intelligent test automation in a DevOps world

Predictive test selection

It is a relatively new approach that adopts ML models to select test cases to run based on an analysis of code changes. Historic code changes and corresponding test case analytics serve as input to the ML model, which understands and incorporates the relationship between the code change characteristics and test cases. The model can then suggest the most apt set of test cases to be run corresponding to a code change, thereby leaving out unnecessary tests saving time and resources. The model is further updated constantly with test results from each run. Google has successfully used this model to reduce the size of its test suite to relevant ones.

Furthermore, organizations have adopted test data generation tools and ML models to predict the minimum set of test cases needed to achieve optimum coverage. Predictability is critical for enabling developers to ascertain a level of coverage for each new code build before it gets committed to a larger codebase.

Identify and obviate flaky tests

Flaky tests can pass and fail at various times, even in the absence of code changes. It’s hard and cumbersome to determine what causes these test failures and often leads to losing multiple run cycles to identify and remedy such tests. ML can play a crucial role in identifying patterns that translate to flaky tests. The cost benefits of such identification are essential, especially in relatively huge test suites, wherein digging to identify the root cause of flakiness can cost dearly. By effectively utilizing ML algorithms’ feedback and learning model, one can identify and address the underlying cause of flakiness, and such tests can be designated into more probable categories.

Bringing intelligence into QA automation for continuous integration and delivery

With the rapid evolution of digital systems, traditional QA automation techniques have been falling behind because of their inability to manage massive datasets. Applications concerned with customer experience, IoT, augmented/virtual reality often encounter exponentially large datasets generated in real-time and across a wide range of formats. The prerequisite of test automation systems that can make a quality difference in this landscape is extensively using data mining, analysis, and self-learning techniques. Not only do they need to utilize mammoth datasets, but they also need to transform test lifecycle automation to one that is adaptive and cognitive.

Digital transformation acts as the accelerator for faster code development with quality assured from the initial stages. Adopting AI/ML/NLP and similar innovative technologies for transforming QA as it adheres to continuous quality code releases are already underway. This is also validated by the World Quality Report 2021-22, which mentions that Smart Technologies in QA and testing are no longer in the future – they’re arriving. Confidence is high, plans are robust, and skills and toolkits are being developed. The sooner organizations adopt these techniques and practices, the faster they can change the contours of their software development release cycles.

Does your QA meet your project needs? Let us assess and redesign it for continuous integration & delivery. Let’s talk

Intelligent Test Automation in a DevOps World

The importance of intelligent test automation

Digital transformation has disrupted time to market like never before. Reducing the cycle time for releasing multiple application versions through the adoption of Agile and DevOps principles has become the prime factor for providing a competitive edge. However, assuring quality across application releases is now proving to be an elusive goal in the absence of the right amount of test automation. Hence it is no surprise that according to the Data Bridge Market research, the automation testing market will reach an estimated value of USD 19.9 billion by 2028 and grow at a CAGR of 14.89% in the forecast period of 2021 to 2028.

Test automation is a challenge, not only because an organization’s capabilities have traditionally been focused on manual testing techniques but also because it’s viewed to be a complex siloed activity. Automation engineers are expected to cohesively bind the vision of the business team, functional flows that the testers use, along their own core component of automation principles and practices. Production continuum can only become a reality when there is a disruption to automation as a siloed activity, ably supported by maximum collaboration and convergence of skillsets. Even then, without the adoption of the right test automation techniques, it becomes near impossible to realize the complete value.

Outlined below are steps towards making test automation initiatives more effective and results-oriented.

Comprehensive coverage of test scenarios

Test automation, to a large extent, focuses on the lower part of the test pyramid addressing viz – unit testing and component testing but neglecting the most crucial aspect of testing business-related areas. The key to assuring application quality is to identify those scenarios that are business relevant and automate them for maximum test coverage. The need of the hour is to adopt tools and platforms that cover the entire test pyramid and not restrict it to any level.

Read more: The right testing strategies for AI/ML applications

A test design-led automation approach can help in ensuring maximum coverage of test scenarios. However, given that this is a complex area, aggravated by the application complexity itself, what tools can help with is to handle the sequence of test scenarios, expressing the business rules and associating data-driven decision tables attached to the workflow, thereby providing complete coverage of all high-risk business cases. By adopting this sequence, complexity can be better managed, modifications can be applied much faster, and tests can be structured to be more automation friendly. 

This approach helps to analyze functional parameters of the test in a better way and helps to define what needs to be tested with sharp focus, i.e., enable a sharper prioritization of the test area. It aggregates various steps involved in test flow along with the conditions each step can have and prioritizes the generation of steps along with risk association.

Ensure 80% test coverage with comprehensive automation testing frameworks. Let’s talk

Sharp focus on test design

The adoption of Test Driven Development (TDD) and Behavior Driven Development (BDD) techniques aims to accelerate the design phase in Agile engagements. However, these techniques come at the cost of incomplete test coverage and test suite maintenance-related issues. Test design automation aims to overcome these challenges by concentrating on areas like requirements engineering, automated test case generation, migration, and optimization. Automation focus at the test design stage contributes to tremendous value-add downstream by removing the substantial load from scripting test cases and generating them. 

Adoption of the right toolsets accelerates the inclusion of test design automation during the earlier stages of the development process, making it key to Agile engagements. Most test design automation tools adopt visual-based testing. They make use of graphical workflows that can be understood by all project stakeholders – testers, business stakeholders, technical experts, etc. Such workflows can be synchronized with any requirements management toolsets and collaboratively improved with inputs from all stakeholders. User stories and acceptance criteria are contextualized so that everyone can see the functional dependency between the previous user stories and the ones that were developed during the current sprint.

Collaboration is key

Collaboration is the pillar of Agile development processes. By bringing collaboration into test design, risk-based coverage of test cases can be effectively addressed, along with the generation of automated scripts on a faster note. Automation techniques steeped in collaboration provide the ability to organize tests by business flows, keywords, impact and ensure depth of test coverage by leveraging the right test data. 

By integrating test automation tools into Agile testing cycles, a collaborative test design can be delivered with ease. With such tools, any changes to user stories can be well reflected; users can comment on the flows or data, identify and flag risks much earlier. These tools also enable the integration of test cases into test management tools of choice like Jira and generate automation scripts that can work under different automation tools like selenium.

Making legacy work

Most organizations suffer from a huge backlog of legacy cases – there is a repository of manual test cases that are critical for business. Organizations need them to be a part of the agile stream. For this to happen, automation is mandatory. Manual test cases of legacy applications are very rich in application functionality and make good sense to get retrofitted into test automation platforms.

New age test design automation frameworks and platforms can address legacy tests that are already documented, parse them, and incorporate them as a part of the automation test suite. Many of these tools leverage AI to retro engineer manual test cases into the software platform – graphical workflow, test data, and test cases themselves can be added to the tool. 

You may also like: Uncovering nuances in data-led QA for AI/ML applications

A closer look at the current test automation landscape outlines a shift from the siloed model that existed earlier. Clearly visible is the move towards automation skillsets, coding practices, and tools-related expertise. Automation tools are also seen moving up the maturity curve to optimize the effort of test automation engineers, at the same time enabling functional testers with minimal exposure to automation stacks to contribute significantly to automation effort. All in all, such shifts are accelerating the move towards providing organizations the ability to do more automation with existing resources.

Trigent’s partnership with Smartesting allows us to leverage test design automation by Integrating these tools in your Agile testing cycles, thus being able to quickly deliver collaborative test design, risk-based coverage of test cases, and faster generation of automated scripts. We help you organize tests by business flows, keywords, risks, depth of coverage, leveraging the right test data, as well as generate and integrate test cases into test management tools of your choice (JIRA, Zephy, Test Rail, etc.).

Our services will enable you to take on your documented legacy tests, parse them and bring them into such tools very quickly. Further, we help you generate test automation scripts that can work under different automation tools like Selenium & Cypress. Our services are delivered in an As-A-Service Model, or you can leverage our support to implement the tools and the training of your teams to achieve their goals.

Ensure seamless functionality and performance of your application with intelligent test automation. Call us now!

Uncovering Nuances in Data-led QA for AI/ML Applications

QA for AI/ML applications requires a different approach when compared to traditional applications. Unlike the latter that has set business rules with defined outputs, the continuously evolving nature of AI models makes their outcomes ambiguous and unpredictable.  QA methodologies need to adapt to this complexity and overcome issues relating to comprehensive scenario coverage, lack of security, privacy, and trust. 

How to test AI and ML applications?

The standard approach to AI model creation, also known as the cross-industry standard process for data mining (CRISP-DM), starts with data acquisition, preparation, and cleansing. The resulting data is then used on multiple model approaches iteratively before finalizing the perfect model. Testing this model starts by using a subset of data that has undergone the process outlined earlier. By inputting this data (test data) into the model, multiple combinations of hyperparameters or variations are run on the model to understand its correctness or accuracy, ably supported by appropriate metrics. 

Groups of such test data are generated randomly from the original data set and applied to the model. Very similar to the new data simulation approach, this process dictates how the AI model will scale in the future with accuracy.

Also Read: How to adopt the right testing strategies to assure the quality of AI/ML-based models

Challenges in data-led QA for AI/ML applications

The data-led testing and QA for AI/ML applications outlined above suffer from myriad issues, some of which are given below.

Explainability

The decision-making algorithms of  AI models have always been perceived to be black boxes. Of late, there is a strong move towards making them transparent by explaining how the model has arrived at a set of outcomes based on a set of inputs. It helps understand and improve model performance and helps recipients grasp the model behavior. This is even more paramount in complaint-heavy areas like insurance or health care systems. Multiple countries have also started mandating that along with the AI model, there needs to be an explanation set on the decisions made.

Post facto analysis is key to addressing explainability. By retrospectively analyzing specific instances misclassified by an AI model, data scientists understand the part of the data set that the model actively focused on to arrive at its decision. On similar lines, positively classified findings are also analyzed.

Combining both helps to understand the relative contribution made by each data set and how the model stresses specific attribute classes to create its decision. It further enables data scientists to reach out to domain experts and evaluate the need to change data quality to get more variation across sensitive variables and understand the need to re-engineer the decision-making parameter set used by the model. In short, the data science process itself is being changed to incorporate explainability.

You may also like: 5 points to evaluate before adopting AI in your organization

Bias

Decision-making ability of an AI model hinges to a large extent on the quality of data that it’s exposed to. Numerous instances show seepage of biases into the input data or how the models are streamed, like Facebook’s gender discriminatory Ads or Amazon’s AI-based automated recruiting system that showed discrimination against women.

The historical data that Amazon used for its system was heavily skewed on account of male domination across its workforce and the tech industry over a decade. Even large models like open AI or codepilot suffer from the percolation of world biases into their models since they are trained on global data sets that are themselves biased. While removing biases, it’s sufficient to understand what has gone into data selection and the feature sets that contribute to decision-making.

Detecting bias in a model mandates evaluating and identifying those attributes that excessively influence the model compared to other attributes. Attributes so unearthed are then tested to see if they represent all available data points. 

Security

According to Deloitte’s State of AI in the Enterprise survey, 62% of respondents view cyber security risks as a significant concern while adopting AI. ‘The Emergence Of Offensive AI’ report from Forrester Consulting found that 88% of decision-makers in the security industry believe offensive AI is coming.

Since AI models themselves are built on the principle of becoming smarter with each iteration of real-life data, attacks on such systems also tend to become smarter. The matter is further complicated by the rise of adversarial hackers whose goal is to target AI models by modifying a simple aspect of input data, even to the extent of a pixel in an image. Such small changes can potentially bring out more significant perturbations in the model, leading to misclassifications and erroneous outcomes.

The starting point for overcoming such security issues is to understand the type of attacks and vulnerabilities in the model that hackers can exploit. Gathering literature on such kinds of attacks and domain knowledge to create a repository that can predict such attacks in the future is critical. Adopting AI-based cyber security systems is an effective technique to thwart hacking attempts since the AI-based system can predict hacker responses very similar to how it predicts other outcomes.

Privacy

With the increased uptake of privacy concerns like GDPR, CCPA across all applications and data systems, AI models have also come under the scanner. More so because AI systems depend heavily on large volumes of real-time data for intelligent decisions – data that can reveal a tremendous amount of information about a person’s demographic, behavior and consumption attributes, at the minimum. 

The AI model in question needs to be audited to evaluate how it leaks information to address privacy concerns. A privacy-aware AI model takes adequate measures to deanonymize, pseudonymize or use cutting-edge technology for differential privacy. By analyzing how privacy attackers get access to input training data from the model and reverse engineer effectively to get access to PII (Personally Identifiable Information), the model can be evaluated for privacy leakage. A two-stage process of detecting the inferable training data by inference attacks and then identifying the presence of PII in the data can help identify privacy concerns when the model is deployed.  

Want to know more? Read: Best practices for test data management in an increasingly digital world

Ensuring accuracy in QA for AI/ML applications

Accurate testing of AI-based applications calls for extending the notion of QA beyond the confines of performance, reliability, and stability to newer dimensions of explainability, security, bias, and privacy. The international standards community has also embraced this notion by expanding the conventional ISO 25010 standard to include the aforementioned facets. As AI/ML model development progresses, focus across all these facets will lead to better performing, continuously learning, a compliant model with the ability to generate far more accurate and realistic results.

Need help? Ensure seamless performance and functionality for your intelligent application. Call us now

DevOps Success: 7 Essentials You Need to Know

High-performing IT teams are always looking for ways to adopt and use industry best practices and solutions. This enables them to overcome obstacles and achieve consistent and reliable commercial outcomes. A DevOps strategy enables the delivery of software products and services to the market in a more reliable and timely manner. The capacity of the team to have the correct combination of human judgment, culture, procedure, tools, and automation is critical to DevOps success.

Is DevOps the Best Approach for You?

DevOps is a solid framework that aids businesses in getting the most out of their digital efforts. It fosters a productive workplace by enhancing cooperation and value generation across all teams, including development, testing, and operations.

DevOps-savvy companies can launch software solutions more quickly into production, with shorter lead times and reduced failure rates. They have higher levels of responsiveness, are more resilient to production difficulties, and restore failed services more quickly.

However, just because every other IT manager is boasting about their DevOps success stories doesn’t mean you should jump in and try your hand at it. By planning ahead for your DevOps journey, you can avoid the traps that are sure to arise.

Here are seven essentials to keep in mind when you plan your DevOps journey.

1. DevOps necessitates a shift in work culture—manage it actively.

The most important feature of DevOps is the seamless integration of various IT teams to enable efficient execution. It results in a software delivery pipeline known as Continuous Integration-Continuous Delivery (CI/CD). Across development, testing, and operations, you must abandon the traditional silo approach and adopt a collaborative and transparent paradigm. Change is difficult and often met with opposition. It is tough for people to change their working habits overnight. You play an important role in addressing such issues in order to achieve cultural transformation. Be patient, persistent, and use continuous communication to build the necessary change in the management process.

2. DevOps isn’t a fix for capability limitations— it’s a way to improve customer experiences

DevOps isn’t a panacea for all of the problems plaguing your existing software delivery. Mismatches between what upper management expects and what is actually possible must be dealt with individually. DevOps will give you a return on your investment over time. Stakeholder expectations about what it takes to deploy DevOps in their organization should be managed by IT leaders.

Obtain top-level management buy-in and agreement on the DevOps strategy, approach, and plan. Define DevOps KPIs that are both attainable and measurable, and make sure that all stakeholders are aware of them.

3. Keep an eye out for going off-track during the Continuous Deployment Run

Only until you can forecast, track, and measure the end-customer advantages of each code deployment in production can you fully implement DevOps’ continuous deployment approach. In each deployment, focus on the features that are important to the business, their importance, plans, development, testing, and release.

At every stage of DevOps, developers, testers, and operations should all contribute to quality engineering principles. This ensures that continuous deployments are stable and reliable.

4. Restructure your testing team and redefine your quality assurance processes

To match with DevOps practices and culture, you must reimagine your testing life cycle process. To adapt and incorporate QA methods into every phase of DevOps, your testing staff needs to be rebuilt and retrained into a quality assurance regimen. Efforts must be oriented toward preventing or catching bugs in the early stages of development, as well as assisting in making every release of code into production reliable, robust, and fit for the company.

DevOps testing teams must evolve from a reactive, bug-hunting team to a proactive, customer-focused, and multi-skilled workforce capable of assisting development and operations.

5. Incorporate security practices earlier in the software development life cycle (SDLC)

Security is typically considered near the end of the IT value chain. This is primarily due to the lack of security knowledge among most development and testing teams. Information security’s confidentiality, integrity, and availability must be ingrained from the start of your SDLC to ensure that the code in production is secure against penetration, vulnerabilities, and threats.

Adopt and use methods and technologies to help your system become more resilient and self-healing. Integrating DevSecOps into DevOps cycles will allow you to combine security-focused mindsets, cultures, processes, tools, and methodologies across your software development life cycle.

6. Only use tools and automation when absolutely necessary

It’s not about automating everything in your software development life cycle with DevOps. DevOps emphasizes automation and the use of tools to improve agility, productivity, and quality. However, in the hurry to automate, one should not overlook the value and significance of the human judgment. From business research to production monitoring, the team draws vital insights and collective intelligence through constant and seamless collaborative efforts that can’t be substituted by any tool or automation.

Managers, developers, testers, security experts, operations, and support teams must collaborate to choose which technologies to utilize and which automation areas to automate. Automate tasks like code walkthroughs, unit testing, integration testing, build verification, regression testing, environment builds, and code deployments that are repetitive.

7. DevOps is still maturing, and there is no standard way to implement it

DevOps is continuously changing, and there is no one-size-fits-all approach or strategy for implementing it. DevOps implementations may be defined, interpreted, and conceptualized differently by different teams within the same organization. This could cause misunderstanding in your organization regarding all of your DevOps transformation efforts. For your company’s demands, you’ll need to develop a consistent method and plan. It’s preferable if you make sure all relevant voices are heard and ideas are distilled in order to produce a consistent plan and approach for your company. Before implementing DevOps methods across the board, conduct research, experiment, and run pilot projects.

(Originally published in Stickyminds)

5 Must-Haves for QA to Fit Perfectly into DevOps

DevOps is the ideal practice for software development businesses that want to code, build, test, and release software continuously. It’s popular because it stimulates cross-skilling and self-improvement by creating a fast-paced, results-oriented, collaborative workplace. QA in DevOps fosters agility, resulting in speedier operational support and fixes that meet stakeholder expectations. Most significantly, it ensures the timely delivery of high-quality software.

Quality Assurance and Testing (QAT) is a critical component of a successful DevOps strategy. QAT is a vital enabler that connects development and operations in a collaborative thread to assure the delivery of high-quality software and applications.

DevOps QA Testing – Integrating QA in DevOps

Five essentials play a crucial role in achieving flawless sync and seamlessly integrating QA into the DevOps process.

1. Concentrate on the Tenets of Testing

Testing is at the heart of QA; hence the greatest and most experienced testers with hands-on expertise must be on board. Some points to consider: the team must maintain a strong focus on risk, include critical testing thinking into the functional and non-functional parts of the application, and not lose sight of the agile testing quadrant’s needs. Working closely with the user community, pay particular attention to end-user experience tests.

2. Have relevant technical skills and a working knowledge of tools and frameworks

While studying and experimenting with the application is required, a thorough understanding of the unique development environment is also required. This guarantees that testers contribute value to the design stage talks and advise the development team on possibilities and restrictions. They must also be familiar with the operating environment and, most significantly, the application’s performance in the actual world.

The team’s skill set should also include a strong understanding of automation and the technologies required, as this adds rigor to the DevOps process and is necessary to keep it going. The QA team’s knowledge must be focused on tools, frameworks, and technologies. What should their automation strategy be? Do they advocate or utilize licensed or open-source software?

Are the tools for development, testing, deployment, and monitoring identified for the various software development life cycle stages? To avoid delays and derailing the process at any stage of development, it is critical to have comprehensive clarity on the use of tools. Teams with the competence should be able to experiment with technologies like artificial intelligence or machine learning to give the process a boost.

3. Agile Testing Methodologies

DevOps is synchronized agility that incorporates development, quality assurance, and operations. It’s a refined version of the agile testing technique. Agile/DevOps techniques now dominate software development and testing. Can the QA team ensure that in an Agile/DevOps environment, the proper coverage, aligned to the relevant risks, enhances velocity? Is the individual or group familiar with working in a fast-paced environment? Do they have the mechanisms to ensure continuous integration, development, testing, and deployment in cross-functional, distributed teams?

4. Industry experience that is relevant

Relevant industry knowledge ensures that the testers are aware of user experiences and their potential influence on business and can predict potential bottlenecks. Industry expertise improves efficiency and helps testers select testing that has the most impact on the company.

5. The Role of Culture

In DevOps, the QA team’s culture is a crucial factor. The DevOps methodology necessitates that QA team members be alert, quick to adapt, ethical, and work well with the development and operations teams. They serve as a link between the development and operations teams, and they are responsible for maintaining balance and ensuring that the process runs smoothly.

In a DevOps process, synchronizing the three pillars (development, quality assurance, and operations) is crucial for software products to fulfill expectations and deliver commercial value. The QA team serves as a link in this process, ensuring that software products transfer seamlessly from development to deployment. What factors do you believe QA can improve to integrate more quickly and influence the DevOps process?

(Originally published in Stickyminds)

Adopt the Right Testing Strategies for AI/ML Applications

The adoption of systems based on Artificial Intelligence (AI) and Machine Learning (ML) has seen an exponential rise in the past few years and is expected to continue to do so. As per the forecast by Markets and Markets, the global AI market size will grow from USD 58.3 billion in 2021 to USD 309.6 billion by 2026, at a CAGR of 39.7% during the aforementioned forecast period. In a recent Algorithmia Survey, 71% of respondents mentioned an increase in budgets for AI/ML initiatives. Some organizations are even looking at doubling their investments in these areas. With the sporadic growth in these applications, the QA practices and testing strategies for AI/ML applications models also need to keep pace.

An ML model life-cycle involves multiple steps. The first is training the model based on a set of feature sets. The second involves deploying the model, assessing model performance, and modifying the model constantly to make more accurate predictions. This is different from the traditional applications, where the model’s outcome is not necessarily an accurate number but can be right depending on the feature sets used for its training. The ML engine is built on certain predictive outcomes from datasets and focuses on constant refining based on real-life data. Further, since it’s impossible to get all possible data for a model, using a small percentage of data to generalize results for the larger picture is paramount.

Since ML systems have their architecture steeped in constant change, traditional QA techniques need to be replaced with those focusing on taking the following nuances into the picture.

The QA approach in ML

Traditional QA approaches require a subject matter expert to understand possible use case scenarios and outcomes. These instances across modules and applications are documented in the real world, which makes it easier for test case creation. Here the emphasis is more on understanding the functionality and behavior of the application under test. Further, automated tools that draw from databases enable the rapid creation of test cases with synthesized data. In a Machine Learning (ML) world, the focus is mainly on the decision made by the model and understanding the various scenarios/data that could have led to that decision. This calls for an in-depth understanding of the possible outcomes that lead to a conclusion and knowledge of data science.

Secondly, the data that is available for creating a Machine Learning model is a subset of the real-world data. Hence, there is a need for the model to be re-engineered consistently through real data. A rigor of manual follow-up is necessary once the model is deployed in order to enhance the model’s prediction capabilities continuously. This also helps to overcome trust issues within the model as the decision would have been taken through human intervention in real life. QA focus needs to be more in this direction so that the model is closer to real-world accuracy.

Finally, business acceptance testing in a traditional QA approach involves the creation of an executable module and being tested in production. This traditional QA approach is more predictable as the same set of scenarios continue to be tested until a new addition is made to the application. However, the scenario is different with ML engines. Business acceptance testing, in such cases, should be seen as an integral part of refining the model to improve its accuracy, using real-world usage of the model. 

The different phases of QA

Three phases characterize every machine learning model creation:

The QA focus, be it functional or non-functional, is applied to the ML engine across these 3 phases.

  • Data pipeline: The quality of input data sets has a significant role in the ability to predict a Machine Learning system. The success of an ML model lies in the testing data pipelines which ensure clean and accurate data availability through big data and analytics techniques.
  • Model building: Measuring the effectiveness of a model is very different from traditional techniques. Out of a specified number of datasets available, 70-80% is used in training the model, while the remaining is used in validating & testing the model. Therefore, the accuracy of the model is based on the accuracy shown on the smaller of datasets. Ensuring that the data sets used for validating & testing the model are representative of the real-world scenario is essential. It shouldn’t come to pass that the model, when pushed into production, will fail for a particular category that has not been represented either in the training or the testing data sets. There is a strong need to ensure equitable distribution and representation in the data sets.
  • Deployment: Since the all-round coverage of scenarios determines the accuracy of an ML model and the ability to do that in real life is limited, the system cannot be expected to be performance-ready in one go. A host of tests need to be done to the system like candidate testing; A/B testing to ensure that the system is working correctly and can ease into a real-life environment. The concept of a sweat drift becomes valid here whereby we arrive at a measure of time by when the model starts behaving reliably. During this time, the QA person needs to manage data samples and validate model behavior appropriately. The tool landscape that supports this phase is still in an evolving stage.

The QA approaches need to emphasize the following for ensuring the development and deployment of a robust ML engine.

Fairness:

The ideal ML model should be nonjudgmental and fair. Since it depends largely on learning based on data received from real-life scenarios, there is a strong chance that the model will be biased if it gets data from a particular category/feature set. For example, if a chatbot with learning ability through ML engine is made live and receives many inputs that are racist, the datasets that are being received for learning by ML engine are heavily skewed towards racism. The feedback loops that power many of these models ensure that racist bias comes into the ML engine. There have been instances of such chatbots being pulled down after noticeable differences in their behavior.

In a financial context, the same can be extended to biases being developed by the model receiving too many loan approval requests from a particular category of requestors, as an example. Adequate efforts need to be made to remove these biases while aggregating or slicing and dicing these datasets and adding them to the ML engine.

One approach that’s commonly followed to remove the bias that can creep into a model is by building another model (an adversary) that understands the potential of bias from the list of various parameters and incorporates that bias within itself. By frequently moving back and forth between these two models with the availability of real-life data, the possibility of a model that removes the bias becomes higher.

Security:

Many ML models are finding widespread adoption across industries and are already beginning to be used in critical real-life situations. The ML model development is very different from that adopted for software development. It is more error-prone on account of loopholes that can cause malleable attacks and a higher propensity to err on the wrong side on account of erroneous input data.

Many of these models do not start from scratch. They are built atop pre-existing models through transfer and learning methods. If created by a malicious actor, these transfer learning models have every possible way of corrupting the purpose of the model. Further, even after the model goes into production, malicious intent data being fed into the model can change the prediction generated by the model.

In conclusion, assuring the quality of AL/ML-based models and engines needs a fundamentally different approach from traditional testing. It needs to be continuously changing to focus on the data being fed into the system and on which predictive outcomes are made. Continuous testing, focusing on the quality of data, the ability to affect the predictive outcome, and remove biases in prediction is the answer.

(This blog was originally published in EuroStar)

QA in Cloud Environment – Key Aspects that Mandate a Shift in the QA Approach

Cloud computing is now the foundation for digital transformation. Starting as a technology disruptor a few years back, it has become the de facto approach for technology transformation initiatives. However, many organizations still struggle to optimize cloud adoption. Reasons abound – ranging from lack of a cohesive cloud strategy to mindset challenges in adopting cloud platforms. Irrespective of the reason, assuring the quality of applications in cloud environments remains a prominent cause for concern.

Studies indicate a wastage of $17.6 billion in cloud spend in 2020 due to multiple factors like idle resources, overprovisioning, and orphaned volumes and snapshots (Source parkmycloud.com). Further, some studies have pegged the cost of software bugs to be 1.1 trillion dollars. Assuring the quality of any application hosted on the cloud not only addresses its functional validation but also its performance-related aspects like load testing, stress testing, capacity planning, etc invariably addressing both the issues described above, thereby exponentially reducing the quantum of loss incurred on account of poor quality.

The complication for QA in cloud-based application arises due to many deployment models ranging from private cloud, public cloud to hybrid cloud, and application service models ranging from IaaS, PaaS, to SaaS. While looking at deployment models, testers will need to address infrastructure aspects and application quality. At the same time, while paying attention to service models, QA will need to focus on the team’s responsibilities regarding what they own, manage, and delegate.

Key aspects that mandate a shift in the QA approach in cloud-based environments are –

Application architecture

Earlier and to some extent even now, when it comes to legacy applications, QA primarily deals with a monolithic architecture. The onus was on understanding the functionality of the application and each component that made up the application, i.e., QA was not just black-box testing. The emergence of the cloud brought with it a shift to microservices architecture, which completely changed testing rules.

Multiple scrum teams work on various application components or modules deployed in containers and connected through APIs in a microservices-based application. The containers have a communication mechanism based on contracts. QA methodology for cloud-based applications is very different from that adopted for monolith applications and therefore requires detailed understanding.

Security, compliance, and privacy

In typical multi-cloud and hybrid cloud environments, the application is hosted in a 3rd party environment or multiple 3rd party environments. Such environments can also be geographically distributed, with data centers housing the information residing in numerous countries. Regulations that restrict data movement outside countries, service models that call for multi-region deployment, and corresponding data storage and access without impinging on regulatory norms need to be understood by QA personnel.QA practitioners also need to be aware of the data privacy rules existing across regions.

The rise of the cloud has given way to a wide range of cybersecurity issues – techniques for intercepting data and hacking sensitive data. To overcome these, QA teams need to focus on vulnerabilities of the application under test, networks, integration to the ecosystem, and third-party software deployed for complete functionality. Usage of tools to simulate Man In The Middle (MITM) attacks helps QA teams identify and overcome any sources of vulnerability through countermeasures.

Building action-oriented QA dashboards need to extend beyond depicting quality aspects to addressing security, infrastructure, compliance, and privacy.

Scalability and distributed ownership

Monolithic architectures depend on vertical scaling to address increased application loads, while in a cloud setup, this is more horizontal in nature. Needless to say that in a cloud-based architecture, there is no limitation to application scaling. Performance testing in a cloud architecture need not consider aspects like breakpoint testing since they can scale indefinitely.

With SaaS-based models, the QA team needs to be mindful that the organization may own some components that require testing. Other components that require testing may be outsourced to other providers, and some of these providers may include cloud providers. The combination of on-premise components and others on the cloud by the SaaS provider makes QA complicated.

Reliability and Stability

This entirely depends on the needs of the organization. An Amazon that deploys 100,000 times a day – features and updates of its application hosted in cloud vis-a-vis an aircraft manufacturer that ensures the complete update of its application before its aircraft is in the air, have diverse requirements for reliability stability. Ideally, testing done for reliability should uncover four categories – what we are aware of and understand, what we are aware of but do not understand, what we understand but are not aware of, and what we neither understand nor are we aware of.

Initiatives like chaos testing aim to uncover these streams by randomly introducing failures through automated testing and scripting and seeing how the application reacts/sustains in this scenario.

QA needs to address the below in a hybrid cloud setup are –

  • What to do when one cloud provider goes down
  • How can the load be managed
  • What happens to disaster recovery sites
  • How does it react when downtime happens
  • How to ensure high availability of application

Changes in organization structure

Cloud-based architecture calls for development through pizza teams, smaller teams fed by one or two pizzas, in common parlance. These micro product teams have testing embedded in them, translating into a shift from QA to Quality Engineering (QE). The tester in the team is responsible for engineering quality by building automation scripts earlier in the cycle, managing performance testing strategies, and understanding how things get impacted in a cloud setup. Further, there is also increased adoption of collaboration through virtual teams, leading to a reduction in cross-functional QA teams.

Tool and platform landscape

A rapidly evolving tool landscape is the final hurdle that the QA practitioner must overcome to test a cloud-based application. The challenge becomes orchestrating superior testing strategies by using the right tools and the correct version of tools. Quick learning ability to keep up with this landscape is paramount. An open mindset to adopt the right toolset for the application is needed rather than an approach steeped with blinders towards toolsets prevailing in the organization.

In conclusion, the QA or QE team behaves like an extension of customer organization since it owns the mandate for ensuring the launch of quality products to market. The response times in a cloud-based environment are highly demanding since the launch time for product releases keeps shrinking on account of demands from end customers and competition. QA strategies for cloud-based environments need to keep pace with the rapid evolution and shift in the development mindset.

Further, the periodicity of application updates has also radically changed, from a 6-month upgrade in a monolith application to feature releases that happen daily, if not hourly. This shrinking periodicity translates into an exponential increase in the frequency of test cycles, leading to a shift-left strategy and testing done in earlier stages of the development lifecycle for QA optimization. Upskilling is also now a mandate given that the tester needs to know APIs, containers, and testing strategies that apply to contract-based components compared to pure functionality-based testing techniques.

Wish to know more? Feel free to reach out to us.

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