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

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

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

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)

The Best Test Data Management Practices in an Increasingly Digital World

A quick scan of the application landscape shows that customers are more empowered, digitally savvy, and eager to have superior experiences faster. To achieve and maintain leadership in this landscape, organizations need to update applications constantly and at speed. This is why dependency on agile, DevOps, and CI/CD technologies has increased tremendously, further translating to an exponential increase in the adoption of test data management initiatives. CI/CD pipelines benefit from the fact that any new code that is developed is automatically integrated into the main application and tested continuously. Automated tests are critical to success, and agility is lost when test data delivery does not match code development and integration velocity.

Why Test Data Management?

Industry data shows that up to 60% of development and testing time is consumed by data-related activities, with a significant portion dedicated to testing data management. This amply validates that the global test data management market is expected to grow at a CAGR of 11.5% over the forecast period 2020-2025, according to the ResearchandMarkets TDM report.

Best Practices for Test Data Management

Any organization focusing on making its test data management discipline stronger and capable of supporting the new age digital delivery landscape needs to focus on the following three cornerstones.

Applicability:
The principle of shift left mandates that each phase in an SDLC has a tight feedback loop that ensures defects don’t move down the development/deployment pipeline, making it less costly for errors to be detected and rectified. Its success hinges to a large extent on close mapping of test data to the production environment. Replicating or cloning production data is manually intensive, and as the World Quality Report 2020-21 shows, 79% of respondents create test data manually with each run. Scripts and automation tools can take up most heavy lifting and bring this down to a large extent when done well. With production quality data being very close to reality, defect leakage is reduced vastly, ultimately translating to a significant reduction in defect triage cost at later stages of development/deployment.

However, using production-quality data at all times may not be possible, especially in the case of applications that are only a prototype or built from scratch. Additionally, using a complete copy of the production database is time and effort-intensive – instead, it is worthwhile to identify relevant subsets for testing. A strategy that brings together the right mix of product quality data and synthetic data closely aligned to production data models is the best bet. While production data maps to narrower testing outcomes in realistic environments, synthetic data is much broader and enables you to simulate environments beyond the ambit of production data. Usage of test data automation platforms that allocates apt dataset combinations for tests can bring further stability to testing.

Tight coupling with production data is also complicated by a host of data privacy laws like GDPR, CCPA, CPPA, etc., that mandate protecting customer-sensitive information. Anonymizing data or obfuscating data to remove sensitive information is an approach that is followed to circumvent this issue. Usually, non-production environments are less secure, and data masking for protecting PII information becomes paramount.

Accuracy:
Accuracy is critical in today’s digital transformation-led SDLC, where app updates are being launched to market faster and need to be as error-free as possible, a nearly impossible feat without accurate test data. The technology landscape is also more complex and integrated like never before, percolating the complexity of data model relationships and the environments in which they are used. The need is to maintain a single source of data truth. Many organizations adopt the path of creating a gold master for data and then make data subsets based on the need of the application. Adopting tools that validate and update data automatically during each test run further ensures the accuracy of the master data.

Accuracy also entails ensuring the relevance of data in the context of the application being tested. Decade-old data formats might be applicable in the context of an insurance application that needs historic policy data formats. However, demographic data or data related to customer purchasing behavior applicable in a retail application context is highly dynamic. The centralized data governance structure addresses this issue, at times sunsetting the data that has served its purpose, preventing any unintended usage. This also reduces maintenance costs for archiving large amounts of test data.

Also important is a proper data governance mechanism that provides the right provisioning capability and ownership driven at a central level, thereby helping teams use a single data truth for testing. Adopting similar provisioning techniques can further remove any cross-team constraints and ensure accurate data is available on demand.

Availability:
The rapid adoption of digital platforms and application movement into cloud environments have been driving exponential growth in user-generated data and cloud data traffic. The pandemic has accelerated this trend by moving the majority of application usage online. ResearchandMarkets report states that for every terabyte of data growth in production, ten terabytes are used for development, testing, and other non-production use cases, thereby driving up costs. Given this magnitude of test data usage, it is essential to align data availability with the release schedules of the application so that testers don’t need to spend a lot of time tweaking data for every code release.

The other most crucial thing in ensuring data availability is to manage version control of the data, helping to overcome the confusion caused by conflicting and multiple versioned local databases/datasets. The centrally managed test data team will help ensure single data truth and provide subsets of data as applicable to various subsystems or based on the need of the application under test. The central data repository also needs to be an ever-changing, learning one since the APIs and interfaces of the application keeps evolving, driving the need for updating test data consistently. After every test, the quality of data can be evaluated and updated in the central repository making it more accurate. This further drives reusability of data across a plethora of similar test scenarios.

The importance of choosing the right test data management tools

In DevOps and CI/CD environments, accurate test data at high velocity is an additional critical dimension in ensuring continuous integration and deployment. Choosing the right test data management framework and tool suite helps automate various stages in making data test ready through data generation, masking, scripting, provisioning, and cloning. World quality report 2020-21 indicates that the adoption of cloud and tool stacks for TDM has witnessed an increase, but there is a need for more maturity to make effective use.

In summary, for test data management, like many other disciplines, there is no one size fits all approach. An optimum mix of production mapped data, and synthetic data, created and housed in a repository managed at a central level is an excellent way to go. However, this approach, primarily while focusing on synthetic data generation, comes with its own set of challenges, including the need to have strong domain and database expertise. Organizations have also been taking TDM to the next level by deploying AI and ML techniques, which scan through data sets at the central repository and suggest the most practical applications for a particular application under test.

Need help? Partner with experts from Trigent to get a customized test data management solution and be a leader in the new-age digital delivery landscape.

4 Rs for Scaling Outsourced QA

The first steps towards a rewarding QA outsourcing engagement

Expanding nature of products, the need for faster releases to market much ahead of the competition, knee jerk or ad hoc reactions to newer revenue streams with products, ever-increasing role of customer experience across newer channels of interaction, are all driving the need to scale up development and testing. With the increased adoption of DevOps, the need to scale takes a different color altogether.

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. The World Quality Report 2020 mentions that 34% of respondents felt QA teams lack skills especially on the AI/ML front. This further reinforces their need to outsource for getting the right mix of skill sets so as to avoid any temporary skill set gaps.

However, ensuring that your outsourced QA gives you speed and scale can be a reality only if the rules of engagement with the partner are clear. Focusing on 4 R’s as outlined below while embarking on the outsourcing journey, will help you derive maximum value.

  1. Right Partner
  2. Right Process
  3. Right Communication
  4. Right Outcome

Right Partner

The foremost step is to identify the right partner, one with a stable track record, depth in QA, domain as well as technology, and the right mix of skill sets across toolsets and frameworks. Further, given the blurring lines between QA and development with testing being integrated across the SDLC, there is a strong need for the partner to have strengths across DevOps, CI/CD in order to make a tangible impact on the delivery cycle.

The ability of the partner to bring to the table prebuilt accelerators can go a long way in achieving cost, time and efficiency benefits. The stability or track record of the partner translates to the ability to bring on board the right team which stays committed throughout the duration of the engagement. The team’s staying power assumes special significance in longer duration engagements wherein shifts in critical talent derail efficiency and timelines on account of challenges involved with newer talent onboarding and effective knowledge transfer.

An often overlooked area is the partner’s integrity. During the evaluation stages, claims pertaining to industry depth as well as technical expertise abound and partners tend to overpromise. Due care needs to be exercised to know if their recommendations are grounded in delivery experience. Closer look at the partner’s references and past engagements not only help to gain insight into their claims but also help to evaluate their ability to deliver in your context.

It’s also worthwhile to explore if the partner is open to differentiated commercial models that are more outcome-driven and based on your needs rather than being fixated on the traditional T&M model.

Right Process

With the right partner on board, creating a robust process and governing mechanism assumes tremendous significance. Mapping key touchpoints from the partner side, aligning them to your team, and identifying escalation points serve as a good starting point. With agile and DevOps principles having collaboration across teams as the cornerstone, development, QA, and business stakeholder interactions should form a key component of the process. While cross-functional teams with Dev QA competencies start off each sprint with a planning meeting, formulating cadence calls to assess progress and setting up code drop or hand-off criteria between Dev and QA can prevent Agile engagements from degrading into mini waterfall models.

Bringing in automated CI/CD pipelines obviates the need for handoffs substantially. Processes then need to track and manage areas such as 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. 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 should focus on integration aspects as well to bridge these gaps. Each team needs to be aware and given visibility on ownership at each stage of the pipeline.

Further, a sound process also brings in elements of risk mitigation and impact assessment and ensures adequate controls are built into SOP documents to circumvent any unforeseen event. Security measures are another critical area that needs to be incorporated into the process early on, more often it is an afterthought in the DevOps process. Puppet 2020 State of DevOps report mentions that integrating security fully into the software delivery process can quickly remediate critical vulnerabilities – 45% of organizations with this capability can remediate vulnerabilities within a day.

Right Communication

Clear and effective communication is an integral component of QA, more so when DevOps, Agile, and similar collaboration-heavy initiatives are pursued achieving QA at scale. Effective communication at the beginning of the sprint ensures 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. From then on, a robust feedback loop, one that aims at continuous feedback and response, cutting across all stages of the value chain, plays a vital role in maintaining the health of the DevOps pipeline.

While regular stand-up meetings have their own place in DevOps, effective communication needs to go much beyond to focus on tools, insights across each stage, and collaboration. A wide range of messaging apps like Slack, email, and notification tools accelerate inter-team communication. Many of these toolkits are further integrated with RSS feeds, google drive, and various CI tools like Jenkins, Travis, Bamboo, etc. making build pushes and code change notifications fully automated. Developers need notifications when a build fails, testers need them when a build succeeds and Ops need to be notified at various stages depending on the release workflow.

The toolkits adopted by the partner also need to extend communication to your team. At times, it makes sense for the partner to have customer service and help desk support as an independent channel to accept your concern. The Puppet report further mentions that companies at a high level of DevOps maturity use ticketing systems 16% more than what is used by companies at the lower end of the maturity scale. Communication of the project’s progress and evolution to all concerned stakeholders is integral irrespective of the platforms used. Equally important is the need to categorize communication in terms of priority and based on what is most applicable to classes of users.

Documentation is an important component of communication and from our experiences, commonly underplayed. It is important for sharing work, knowledge transfer, continuous learning, and experimentation. Code that is well documented enables faster completion of audit as well. In CI/CD-based software release methodology, code documentation plays a strong role in version control across multiple releases. Experts advocate continuous documentation as core communication practice.

Right Outcome

Finally, it goes without saying that setting parameters for measuring the outcome, tracking and monitoring those, determines the success of the partner in scaling your QA initiatives. Metrics like velocity, reliability, reduced application release cycles and ability to ramp up/ramp down are commonly used. Further, there are also a set of metrics aimed at the efficiency of the CI/CD pipeline, like environment provisioning time, features deployment rate, and a series of build, integration, and deployment metrics. However, it is imperative to supplement these with others that are more aligned to customer-centricity – delivering user-ready software faster with minimal errors at scale.

In addition to the metrics that are used to measure and improve various stages of the CI/CD pipeline, we also need to track several non-negotiable improvement measures. Many of these like deployment frequency, error rates at increased load, performance & load balancing, automation coverage of delivery process and recoverability helps to ascertain the efficiency of QA scale up.

Closely following on the heels of an earlier point, an outcome based model which maps financials to your engagement objectives will help to track outcomes to a large extent. While the traditional T&M model is governed by transactional metrics, project overlays abound in cases where engagement scope does not align well to outcome expectations. An outcome-based model also pushes the partner to bring in innovation through AI/ML and similar new-age technology drivers – providing you access to such skillsets without the need for having them on your rolls.

If you are new to outsourcing or working with a new partner, it may be good to start with a non-critical aspect of the work (for regular testing or automation), establish the process and then scale the engagement. For those players having maturity in terms of adopting outsourced QA functions in some way or the other, the steps outlined earlier form an all-inclusive checklist to ensure maximization of engagement traction and effectiveness with the outsourcing partner.

Partner with us

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 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.

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QA outsourcing in the World of DevOps – Best Practices for Dispersed (Distributed) QA Teams

DevOps is the preferred methodology for software development and release, with collaborating teams oriented towards faster delivery cycles augmented by early feedback. QA is a critical binding thread of DevOps practice, with early inclusion at the story definition stage. Adoption of a distributed model of QA and QA outsourcing had earlier been bumpy, however, the pandemic has evened out the rough edges.

Why QA outsourcing is good for business

The underlying principle which drives DevOps is collaboration. With outsourced QA being expedited through teams distributed across geographies and locations, a plethora of 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.

As with everything in life, DevOps needs a balanced approach, 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 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; good automation skills (not necessarily in the same person). In addition, specialist skills related to performance, security, 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. As per a 451 research survey, 39% of respondents juggle 11 to 30 tools so as to keep an eye on their application infrastructure and cloud environment; 8% are even 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 towards the tool mix, ideally by influencing the team to adopt a common set of tools instead of making it mandatory.

Ensure a robust CI/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 identification of environment configurations. These can ultimately translate into failed tests and thereby failed delivery/deployment. A well-defined automated process ensures continuous deployment & monitoring throughout the lifecycle of an application, from integration and testing phases through to release & 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 infra support can hamper the productivity of distributed teams. When strengthened by remote alerts and 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 & deployment practices, as well as establishing a workflow for defect/issue management, are paramount in ensuring the effectiveness of distributed DevOps. 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 key 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. Recent research from 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 show that 63 percent start to test only after a new build and code is being developed. Just 40 percent test upon each code change or at the start of new software.

Devote equal attention to both manual and 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 with improving coverage for repeatable tasks. Planning for both during your early sprint planning meetings is important. In most cases, automation is usually given step-motherly treatment and falls at the wayside due to scope creep and repeated testing due to defects.

A 2019 state of testing report, shows that only 25 percent of respondents claimed they have more than 50 percent of their functional tests automated. So, the ideal approach would be to separate the two sets of activities and ensure that they both get equal attention from their own set of specialists.

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 DevSecOps Community Survey, 55 percent of respondents deploy at least once per week, and 18 percent claim multiple daily deployments. But when it comes to security, 45 percent of the survey’s respondents 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 the 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

In order 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. It is heartening to note that the recent pandemic situation has revealed a positive trend in terms of better acceptance of these practices. 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 responsible testing practices put process before convenience to delight stakeholders with an impressive industry rivaled Defect Escape Ratio or DER of 0.2.

Test our abilities. Contact us today.

Responsible Testing – Human centricity in Testing

Responsibility in testing – What is responsible testing?

Consumers demand quality and expect more from products. The DevOps culture emphasizes the need for speed and scale of releases. As CI/CD crisscrosses with quality, it is vital to engage a human element in testing to foresee potential risks and think on behalf of the customer and the end-user.

Trigent looks at testing from a multiplicity of perspectives. Our test team gets involved at all stages of the DevOps cycle, not just when the product is ready. For us, responsible testing begins early in the cycle.

Introducing Quality factor in DevOps

A responsible testing approach goes beyond the call of pre-defined duties and facilitates end-to-end stakeholder assurance and business value creation. Processes and strategies like risk assessment, non-functional tests, and customer experiences are baked into testing. Trigent’s philosophy of Responsible Testing characterizes all that we focus on while testing for functionality, security, and performance of an application.

Risk coverage: Assessing the failure and impact early on is one of the most critical aspects of testing. We work along with our clients’ product development teams to understand what’s important to stakeholders, evaluate and anticipate risks involved early on giving our testing a sharp focus.

Collaborative Test Design: We consider the viewpoints of multiple stakeholders to get a collaborative test design in place. Asking the right questions to the right people to get their perspectives helps us in testing better.

Customer experience: Responsible Testing philosophy strongly underlines customer experience as a critical element of testing. We test for all promises that are made for each of the customer touchpoints.

Test early, test often: We take the shift-left approach early on in the DevOps cycle. More releases and shorter release times mean testing early and testing often translates into constantly rolling out new and enhanced requirements.

Early focus on non-functional testing: We plan for the non-functional testing needs at the beginning of the application life cycle. Our teams work closely with the DevOps team’s tests for security, performance, and accessibility – as early as possible.

Leverage automation: In our Responsible Testing philosophy, we look at it as a means to get the process to work faster and better. Or to leverage tools that can give better insights into testing, and areas to focus on testing. The mantra is judicious automation.

Release readiness: We evaluate all possibilities of going to the market – checking if we are operationally ready, planning for the support team’s readiness to take on the product. We also evaluate the readiness of the product, its behavior when it is actually released, and prepare for the subsequent changes expected.

Continuous feedback: Customer reviews, feedback speaks volumes of their experience with the application. We see it as an excellent opportunity to address customer concerns in real-time and offer a better product. Adopting the shift-right approach we focus on continuously monitoring product performance and leveraging the results in improving our test focus.

Think as a client. Test as a consumer.

Responsibility in testing is an organizational trait that is nurtured into Trigent’s work culture. We foster a culture where our testers imbibe qualities such as critical thinking on behalf of the client and the customer, the ability to adapt, and the willingness to learn.

Trigent values these qualitative aspects and soft skills in a responsible tester that contribute to the overall quality of testing and the product.
Responsibility: We take responsibility for the quality of testing of the product and also the possible business outcomes.

Communication: In today’s workplace, collaborating with multiple stakeholders, teams within and outside the organization is the reality. We emphasize not just the functional skill sets but the ability to understand people, empathize with different perspectives, and express requirements effectively across levels and functions.

Collaboration: We value the benefits of a good collaboration with BA/PO/Dev and QA and Testing – a trait critical to understanding the product features, usage models, and working seamlessly with cross-functional teams.

Critical thinking: As drivers of change in technology, it is critical to develop a mindset of asking the right questions and anticipating future risks for the business. In the process, we focus on gathering relevant information from the right stakeholders to form deep insights about the business and consumer. Our Responsible Testing approach keeps the customer experience at the heart of testing.

Adaptability & learning: In the constantly changing testing landscape, being able to quickly adapt to new technologies and the willingness to learn helps us offer better products and services.

Trigent’s Responsible Testing approach is a combination of technology and human intervention that elevates the user experience and the business value. To experience our Responsible Testing approach, talk to our experts for QA & Testing solutions.

Learn more about responsible testing in our webinar and about Trigent’s software testing services.

Accelerate CI/CD Pipeline Blog Series – Part 1- Continuous Testing

Given its usefulness in software development, Agile methodologies have come to be embraced across the IT ecosystem to streamline processes, improve feedback, and accelerate innovation.

Organizations now see DevOps as the next wave after Agile that enables Continuous Integration and Continuous Delivery (CI/CD).  While Agile helped streamline and automate the entire software delivery lifecycle, CI/CD goes further. CI checks the code often, and the tested chunks are integrated, sometimes several times in a single day, to create a stream of smaller and frequent releases through CD.

As a principal analyst at Forrester Research puts it succinctly: ”If Agile was the opening act, continuous delivery is the headliner. The link that enables CI/CD, however, is Continuous Testing (CT).

What is Continuous Testing?

Continuous Testing is a process by which feedback on business risks of a software release is acquired as rapidly as possible. It helps in early risk identification & incremental coverage as it’s integrated into the delivery pipeline. Continuous Testing is achieved by making test automation an integral part of the software delivery pipeline. It’s seamlessly interwoven into the software delivery pipeline (not tagged at the end).

Though CI/CD enables speed-to-market, inadequate end-to-end experience testing can turn it into a liability.  A key aspect of CT is to leverage test automation to enable coverage and speed.

Test Automation – Continuous Testing’s Secret Success Factor

Automation of tests is the key to ensure that Quality Assurance is as continuous, agile, and reliable.  CT involves automating the tests and running them early and often.  It leverages service virtualization to increase the test coverage when parts of the business functions are available at different points in time.

Automated Testing binds together all the other processes that comprise the CD pipeline and makes DevOps work. By validating changing scenarios, Smart automation helps in faster software delivery.

In part II of the blog series we will talk more about why test automation is essential for CI/CD Testing, and automation framework.

Learn more about Trigent software testing services or test automation services

Outsourcing Testing in a DevOps World

Software products today are being developed for a unified experience. Applications are created to perform and deliver a seamless experience on multiple types of devices, operating on various platforms.

Additionally, the growing demand for launching products at pace and scale is pushing businesses towards ensuring that they are market-ready in shorter time frames. The prevalence of Agile/DevOps practices now requires testing to be carried out simultaneously to development. Continuous development, integration, testing, and deployment have become the norm. Testers are now a part of the development process, testing the features, releases, and updates in parallel as they get developed.

The testing & deploying of a multi-platform product in a fast-paced environment requires expertise and complimenting infrastructure to deliver a unified experience. Add multiple product lines, constant updates for new features, a complex deployment, a distributed user base, into the mix, and your search for an outsourcing partner could become a daunting task.

We share some considerations that can guide your decision making — drawn from our experience of working as outsourcing partners for some of our clients, helping them deliver quality products on time.

Criteria our clients applied before selecting us as their outsourcing testing partners

Need for staff augmentation vs. managed services

You can choose staff augmentation if the requirement is short term and the tasks are well defined. In the case of a long term project, it is best to opt for managed services. Managed services suit best if the project requires ongoing support and skill sets that are not available with the business but are vital for the product or project. It also fits well for long term projects that have a clear understanding of outputs and outcomes.

Agility of the vendor’s testing practices

Agile/DevOps methodologies now drive a healthy percentage of software development and testing. Can the vendor maintain velocity in an Agile/DevOps environment? Do they have the processes to integrate into cross-functional, distributed teams to ensure continuous integration, development, testing, and deployment?

Relevant experience working for your industry

Relevant industry experience ensures that the testers involved know about your business domain. Industry knowledge not only increases efficiency but also guides testers to prioritize testing with the highest level of business impact.

Tools, frameworks, and technologies that the vendor offers

Understand the expertise of the vendor in terms of the tools, frameworks, and technologies. What is their approach to automation? Do they use/recommend licensed or open source tools? These are some considerations that can guide your evaluation.

Offshoring – Onshoring – Bestshoring

Many vendors recommend offshoring processes to reap benefits from cost savings. But does offshoring translate to an equally beneficial proposition for you? While you can best ascertain the applicability and benefits of offshoring, it is advisable to go for a mix of the three. In a managed services engagement, right shoring (a mix of onsite & offshore), ensures that the coordination aspects are dealt with by the vendor.

Reputation in the market

Ascertaining the reputation of the vendor in the market can be another useful way of evaluation. Reading independent reviews about the organization, understanding the longevity of their existing engagements (Customer stickiness), references from businesses that have engaged with the vendor earlier, number of years the organization has been in business are some of the factors that can be applied.

Culture within the organization

The culture of your potential partner must be broadly aligned with your organizational culture. The vendor should identify with your culture, be quick to adapt, be ethical, and gel well with the existing team. The culture within the vendor organization is also crucial to ensure that the employees are respectful of their commitments and stay accountable for assigned responsibilities.

Low-cost vs. high-quality output

Engaging with people who have the experience in the required domain and technology, working on a customized delivery model, within stipulated timelines, come for a premium. But are more likely to deliver value compared to a low-cost fragmented solution with inexperienced manpower, little or zero knowledge on domain-specific skills and technology, and an unsure commitment to timely delivery.

Do you know of other factors that influence decision making in terms of identifying the right outsourcing partner? Please share your thoughts.

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