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

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

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

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

Test coverage

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

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

Test reliability

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

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

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

Time to test

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

Scope of automation

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

Time to fix

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

Escaped bugs

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

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

Is your Vendor up to the mark?

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

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

Communication and people enablement

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

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

Process Governance 

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

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

Testing Quality 

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

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

Test Automation 

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

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

Improve your QA effectiveness with Trigent

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

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

Ensure the QA effectiveness and application performance. Talk to us

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

Why Quality Assurance (QA) outsourcing is good for business

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

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

Best practices for ensuring the effectiveness of distributed QA teams

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

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

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

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

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

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

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

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

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

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

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

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

Benefits of outsourcing your QA

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

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

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

5 Ways You are Missing Out on ROI in QA Automation

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

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

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

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

Limitations of the conventional model to compute Test Automation ROI

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

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

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

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

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

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

Test Automation Framework – Build vs. Partner

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

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

Create test scripts faster with ‘Accelerators’

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

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

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

Automate Test Design, Execution and Maintenance

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

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

Shift Performance Testing left with Automation

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

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

Automate deployment of Test Data Sets

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

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

Maximize your ROI with Trigent

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

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

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

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

Why Quality Assurance and Engineering?

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

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

Developing a sound Quality Assurance (QA) strategy

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

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

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

Key performance metrics for QA

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

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

1) Reduced risk build-on-build:

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

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

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

2) Automated tests

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

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

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

While measuring this metric, we recommend taking into account:

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

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

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

4) Analyzing the execution time of test cycles:

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

5) Summary of active defects

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

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

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

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

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

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

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

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

The importance of an optimal QA DevOps strategy

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

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

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

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

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

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

5 QA testing strategies to accelerate CI/CD cycle

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

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

Validate API performance with API testing

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

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

Ensure flawless user experience with Automated GUI testing

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

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

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

Handle unscheduled outages with Non-functional testing

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

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

Improve application security with App Sec testing

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

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

Secure end-to-end functionality with Regression testing 

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

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

Accelerate your application rollout with Trigent’s QA services

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

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

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

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

Intelligent quality engineering (QE) in continuous integration and delivery

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

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

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

Intelligent prioritization for continuous integration and continuous delivery with QE

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

Read more: Intelligent test automation in a DevOps world

Predictive test selection

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

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

Identify and obviate flaky tests

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

Bringing intelligence into QA automation for continuous integration and delivery

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

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

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

Intelligent Test Automation in a DevOps World

The importance of intelligent test automation

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

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

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

Comprehensive coverage of test scenarios

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

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

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

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

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

Sharp focus on test design

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

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

Collaboration is key

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

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

Making legacy work

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

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

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

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

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

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

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

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.

Contact us now.

Accelerate CI/CD Pipeline Blog Series – Part II – Test Automation

In part I of this blog series we spoke about Continuous Testing (CT), CI CD and that Test Automation is a key to its success, how to leverage test automation to enable coverage and speed. Let’s get an in-depth understanding of why it’s essential.

Why Automation of Testing is Essential for CI CD

Code analysis tools, API testing tools, API contract testing tools, Service virtualization tools, performance assessment tools, and end-to-end automation tools are all parts of CT. However, test automation is one of the key enablers of CT as:

  • It allows us to execute tests in parallel, across multiple servers/containers, speeding up the testing process.
  • It frees engineers from repetitive tasks, enabling them to focus on value-adds.
  • It helps validate the impact of even minor changes continuously.

Characteristics of a Good Automation Framework

For test automation to be effective, it must be supported by efficient frameworks. The frameworks provide a structure and help integrate and realize the efforts of a distributed team.

  • Test frameworks must support various languages, browsers, and techniques to make them future-proof. They must possess an Agile testing environment to support the continuous delivery pipeline.
  • The testing platform must be scalable to support as many tests as needed.
  • The platform must support compatibility tests on simulated devices to cut down the cycle.
  • The environment should maximize automation, i.e., trigger tests, analyze results, and share test information across the organization, in a fully automated manner.
  • The testing platform must include security features such as encryption of test data and access control policies

Characteristics of Trigent’s AutoMate Test Automation Framework

Reinforced by globally renowned partners, Trigent’s testing focus is aligned with the current business environment and offers cost benefits, performance, and agility.

As niche test automation experts, we have significant experience in open source and commercial testing tools. Our extensive library of modular, reusable, and resilient frameworks simplifies scenario-based automation. We provide on-demand testing and next-gen scheduling.

Features and benefits
  • Accelerated script development: Script/test cases development effort reduced up to 60-80% in comparison to traditional test automation approaches. Reduces Test Scripting Complexity.
  • Modular and reusable framework components: Reduced dependency on tool-specific resources. Ability to kick-start automation quickly. It supports reusable components. Reduced Test Automation maintenance costs. Allows multi-browser, multi-device testing.
  • Easy test script maintenance: Ease of test execution. Easy to make changes and maintain scripts in the long run. Improved error and exception handling. Supports multiple scripting languages.
  • On-demand Testing: Sanity, Smoke, Integration, Regression, etc. Provides effective test data creation on the go.
  • Hybrid Model: Modular test framework built using JUnit or TestNG. Integrates with multiple test automation tools. Allows a common way of handling multiple test types.
  • Scheduling and customized reporting: Send test results to ALM. Integrates with Test Management tools to track your test plans effectively.
  • Leverages new tech trends: Leverages AI/ML utilities & tools that allow for effective Test Impact analysis and test selection. Integrates with CI/CD tools to enable automated executions. Allows parallel executions to reduce test execution time

Learn more about Trigent software testing services or test automation 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

Why is Test Automation the Next Big Game-changer in the Insurance Industry?

In a world where technology is creating on-demand and bespoke experiences, the insurance industry is ready for disruption. Characterized until now by lengthy and manual processes and limited offerings, the industry has been quick to burst out of its protective bubble by leveraging new-age technologies including Blockchain, Artificial Intelligence (AI), extended reality, and quantum computing.

Key to accelerating this technology disruption is the InsurTech sector seen collaborating with insurance companies to facilitate nimbly and point solutions across the entire value chain. PwC Australia’s Global InsurTech report projects that the number of insurance providers partnering with InsurTechs will reach 84% between 2020 and 2022.[1]

However, in the race to transform their platforms and products, insurance companies—and their InsurTech partners—are facing several challenges. Primary among them are software and application testing challenges.

Insurers and InsurTechs experience different ‘testing’ difficulties

Digital self-service claims platforms, apps that reward policyholders based on their lifestyle and driving behaviors, and even wearables that monitor diabetes and reward condition management, are some of the trending innovations created by InsurTechs.[2] The companies behind these innovations mostly characterize themselves as digital economies. They evolve faster, are more embracing of Agile & DevOps, and have built products from the ground up are usually less complicated from the product stack that they manage. So when it comes to testing, their primary need is speed. To get to the market first, they need to identify failures, test, and evaluate faster than their competitors.

Furthermore, InsureTech players see themselves primarily as product/platform developers. This requires them to customize and dovetail their product into other complex systems. Testing, therefore, will have to extend beyond their primary product stack and include integration as well.

Insurance companies, on the other hand, have a completely different set of testing needs. Equipped with a substantial customer base, but challenged with changing customer needs, they are transforming to meet the digital, omnichannel, tailored offerings that can enrich the customer journey and experience. This leads them to integrate newer systems into their existing technology stack.

Trigent has successfully worked with several insurance companies and InsurTechs to shrink development cycles and increase the speed-to-market of applications. Contact us to know more about our AI-enabled test automation solution.

Testing needs will then have to be heavily focused on end-to-end validations to ensure that customer experiences are not compromised. Additionally, testing will also have to ensure that integration with complex legacy applications is maintained. At the same time, the performance and security of valuable customer data is not compromised when supporting their large customer bases.

Test automation—an essential tool in accelerating disruption

Test automation solves several of the above challenges. According to the World Quality Report 2019-2020[3], which surveyed 1725 CIOs and senior tech leaders, the main benefits reported were better control and transparency of test activities and detection of defects. The respondents also attributed test automation to gaining reduced test costs, test cycle time, and security risk.

For InsurTechs, like any other product innovator, deploying test automation fulfills an essential requirement—it assures improved coverage of their checks, ensuring faster release schedules. More importantly, test automation serves as a strong foundation during the integration of their product with larger insurance players. The features of a full-stack test automation framework, such as cross-technology and cross-platform readiness, and library customization, ensures that the marriage of services play with product play is effectively implemented.

Insurance companies leveraging test automation have experienced quantifiable benefits. A leading benefits administration and general insurance agency leveraged test automation software and reduced regression testing time by 85%. The process which covered 95% of the test coverage area led to a 45% reduced time-to-market rate.[4]

Test automation also plays a vital role in their transformation journey. For instance, when insurance companies are integrating new technologies, it simultaneously increases the testing workload. Vendors who manage their test outsourcing contracts are more interested in maintaining their SLAs and thus are slower in the uptake of the new changes. Test automation, essential to conduct acceptance tests is then best addressed through outside, neutral parties.

The PwC Insurance 2020 report[5] states that disruption is the new reality in the global insurance industry requiring industry players to keep pace with the sweeping social, technological, environmental, economic, and political developments that are categorizing this change. Implementing early test automation, at this juncture, will then work as the key differentiator for insurance companies and InsurTechs seeking to gain a head start in the digital ecosystems of the future.

Learn more about Trigent software testing services or test automation services

—————————————————————————————
[1] https://www.pwc.com.au/
[2] https://assets.kpmg/
[3] https://content.microfocus.com/
[4] https://www.trigent.com/
[5] https://www.pwc.co.za/en/

Getting Started – Selenium with Python Bindings

Introduction

Selenium Python binding provides a simple API to write functional/acceptance tests using Selenium WebDriver. Through Selenium Python API you can access all functionalities of Selenium WebDriver in an intuitive way. Selenium Python bindings also provide a convenient API to access Selenium WebDrivers such as Firefox, IE, Chrome, etc. The current supported Python versions are 2.7, 3.5, and above. In this blog, I will explain the Selenium 3 WebDriver API and in the next one, I will explain how to install and configure PyDev in Eclipse.

What is Python?

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. It’s high-level built in data structures, combined with dynamic typing and binding, makes it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy-to-learn syntax emphasizes readability, and, therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form, free of charge, for all major platforms, and can be freely distributed.

Related: Define, Implement, Migrate and Script Automated Tests for End-to-end Automation of Applications Spanning Multiple Technologies.

What is Selenium?

Selenium is an open-source automation tool to test your web application. You can do this in various ways. For instance:

  • Selenium supports multiple languages such as Java, C#, Python, Ruby etc..
  • Selenium has components like, IDE, WebDriver.

Downloading Python bindings for Selenium

You can download Python bindings for Selenium from the PyPI page for the Selenium package. However, a better approach would be to use pip to install the selenium package. Python 3.6 has pip available in the standard library. Using pip, you can install selenium like this: `pip install selenium’. You may consider using virtualenv to create isolated Python environments. Python 3.6 has pyvenv which is almost the same as virtualenv.

Detailed instructions for Windows users

Note: You should have an Internet connection to perform this installation.

  1. Install Python 3.6 using the MSI available in python.org download page.
  2. Start a command prompt using the cmd.exe program and run the pip command as given below to install selenium.

C:Python35Scriptspip.exe install selenium.

Advantages of Python in Selenium

  1. Compared to other languages, Python takes less time to run the script and complete the execution.
  2. Python uses indentation, not braces ({}), making it easy to understand the code flow.
  3. Python is simpler and more compact.

Simple Usage

If you have installed Selenium Python bindings, you can start using it from Python in the following way:

from selenium import webdriver
 from selenium.webdriver.common.keys import Keys
 driver = webdriver.Firefox()
 driver.get("http://www.python.org")
 assert "Python" in driver.title
 elem = driver.find_element_by_name("q")
 elem.clear()
 elem.send_keys("pycon")
 elem.send_keys(Keys.RETURN)
 assert "No results found." not in driver.page_source
 driver.close()

If you found this interesting, don’t miss my next blog, ‘Install and Configure PyDev in Eclipse’.

Read Other Selenium related blogs:

Web Application Testing with Selenium WebDriver

Introduction to Selenium with C Sharp

Introduction to Selenium with C Sharp

Selenium WebDriver is a free and open-source library for automated testing web applications. Selenium tests can be written in multiple programming languages such as Java, C#, Python and Ruby and multiple browsers we execute scripts.

Prerequisite:

  • Download and install Visual Studio Frame Work.
  • Your target browser is installed, such as Chrome or Firefox.

Set up Visual Studio Solution:

Create a new project in Visual Studio Select template ‘Templates’ → ‘Visual C#’ → ‘Test’ → ‘Unit Test Project’.

Add Selenium WebDriver package to the project.

Run the following command in ‘Package Manager Console’ (selecting menu ‘Tools’ → ‘NuGet Package Manager’ → ‘Package Manager Console’).

After Selenium WebDriver is installed,  Solution References will appear like the following:

How to Create a test and run it:

  1. Add a new C# (a test) Class. Right mouse click the project name (in Solution Explorer) select ‘Add’ → ‘Unit Test’.
  2. Define the following class code to test and run it.
using System;
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using OpenQA.Selenium;
 using OpenQA.Selenium.Chrome;
 using OpenQA.Selenium.Firefox;
 using OpenQA.Selenium.Safari;

namespace TestAppDemo
{
[TestClass]
public class UnitTest2
{
static IWebDriver driverGC;
// static IWebDriver driverFF;
[AssemblyInitialize]
public static void setup(TestContext contest)
{
driverGC = new ChromeDriver(@”D:Documents StudySelenium detailschromedriver_win32″);
}
[TestMethod]
public void TestChromeDriver()
{
driverGC.Navigate().GoToUrl(“http://automationpractice.com/index.php”);
driverGC.FindElement(By.Id(“search_query_top”)).SendKeys(“CASUAL DRESSES”);
driverGC.FindElement(By.Id(“search_query_top”)).SendKeys(Keys.Enter);

}
[TestMethod]
public void TestClose()
{
driverGC.Quite();
}

}
}

Right Click on `class’ and select ‘Run Tests’.

After running successful, test case, it will show as below with pass mark:

We have to understand the below elements and their usages:

  • By Id: Finds the Webpage element by its unique control id.
  • By Name: Finds the Webpage element by the control name.
  • By Class: Finds the Webpage element by the Class name.
  • By LinkText: Finds the link element by its exact text displayed on web page
  • By partialLinkText: Finds webpage element that contains the given text
  • By TagName: Finds the Webpage element by the Tag name.
  • By XPath: This is most popular and majorly used for locating webpage element in a easiest way.
  • By CSS selector: Finds Webpage element by the CSS selectorEx.

What are the Advantages of Selenium Test using C#:

  • It is Open source.
  • It can be extended with different technologies.
  • In multiple browsers we execute scripts.
  • It supports multiple OS platforms and compatible with mobile devices.
  • Test executes within the browsers, so focus is not needed while executing the script.
  • Using Selenium Grids, you can execute tests in parallel.
  • Web Driver is faster than Selenium RC because it has simpler architecture.
  • Web Driver directly talks to the web browser whereas Selenium RC needs the help of the RC Server in order to do the same.

Disadvantages of Selenium Test:

  • It supports only web based applications.
  • It does not provide any facility for Object Repository.
  • It does not provide any facility for Recovery Scenario.
  • There is no report generation for test methods by default.
  • Users need to depend on minimum one programming language.

For more details, you can go through this click here.

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Working with Test Driven Development

A technique for using automated unit test scenarios to design and decoupling of dependencies is called Test Driven Development. This technique is heavily used in Agile development methodologies. To use this technique with Visual Studio Team System, you have to understand the following topics:

  • Creating and running automated tests inside the VSTS IDE
  • Abstracting dependencies in an object-oriented world
  • Re-factoring new and old features to remove duplication in the code

The main theme of TDD is “Red, Green, Refactor.”

  • To work with TDD you must have a Test Project solution for creating new test cases in VSTS and it should reference the class library where you are adding new functionality.

Follow these steps to perform TDD:

  1. Understand the requirements of the user story and feature that you are working on.
  2. Red: Create a test and make it fail.
    1. Write the test for already existed code and change the logic of the code to make our test fail.
    2. Run the test case. It should fail. This means your test is calling the correct code and that the code is not working by properly. This is a meaningful failure, and you expect it to fail.
  3. Green: Make the test pass
    1. Initially hard code the expected return value to verify success after that write the correct logic of code to make the test pass.
    2. If you’ve written the code so that the test passes as intended, you are finished. You do not have to write more code speculatively. If new functionality is still needed, then another test is needed. Make this one test pass and continue.
    3. When the test passes, you might want to run all tests up to this point to build confidence that everything else is still working.
  4. Refactor: Change the code to remove duplication in your project and to improve the design while ensuring that all tests still pass.
    1. Remove duplication caused by the addition of the new functionality
    2. Make design changes to improve the overall solution.
    3. After each refactoring, rerun all the tests to ensure that they all still pass.

Advantages of Test-Driven Development

  • When the unit test case passes success and logic of the code is refactored to remove duplicate code.
  • The unit tests will not expire until you separate documentation and this will feedback that each component is still working.
  • TDD will help the Developer to understand the complete design and critical analysis of requirements to work with the logic of code and get an accurate result.
  • Any software developed using TDD can be maintained easily, better designed.
  • If a bug is found, the developer should create a test to reveal the bug and then modify the logic of the code until successive test run, all old bugs is verified and Reduced debugging time.

Limitations

  • Due to extensive use of test cases, all code logic is not executed success or failure properly.
  • Some Security Issues for Integration testing and Compliance testing

Read about Behavior Driven Development here.

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Cucumber Test Automation Framework

Introduction:

Cucumber is a testing framework that helps to bridge the gap between software developers and business managers. Tests are written in plain language, based on the Behavior Driven Development (BDD) style of Given, When, Then, which anyone can understand. It is defined by a Language called “Gherkin”

Difference between TDD and BDD:

TDD: TDD (Test Driven Development) is an iterative development process. Each iteration starts with a set of tests written for a new piece of functionality.

BDD: BDD (Behavior Driven Development) is a synthesis and refinement of practices stemming from TDD. Like in TDD, in BDD also, we write tests first and then add application code.

The main objective of this automation framework is to provide the test script development environment, which is BDD based.

The major differences that we get to see here are:

  • Tests are written in plain descriptive English type grammar.
  • Tests are explained as the behavior of the application and are more user-focused.
  • Using examples to clarify requirements.

Prerequisites:

  • Install JDK and Eclipse on your machine
  • Install Cucumber in Eclipse.
  • Add J Unit to “build-path” in Eclipse.
  • Add Web Driver jar files to “build-path” in Eclipse.

What is “Gherkin”?

Gherkin is a simple, lightweight, and structured language that uses regular spoken language to describe requirements and scenarios.

Gherkin Keywords:

  • Feature: Each Gherkin file begins with a ”Feature” keyword. Feature defines the logical test functionality you will test in this feature file. e.g. if you are testing a “payment gateway”, your Feature will become “Payment Gateway”.
  • Scenario: Each Feature will contain some tests to test the feature. Each test is called a Scenario and is described using the Scenario keyword.
  • Given: Defines a “precondition” to the test.
  • When: This keyword defines the test activities that will be executed. By test action, we mean the user input action.
  • Then: This keyword defines the outcome of previous steps.
  • And: This keyword is used to add conditions to your steps.
  • But: This keyword is used to add negative type comments.
  • *: This keyword is very special. It defines the whole purpose of having Given When Then and all the other keywords.

Example:

Feature: Login Action Test
Description: This feature will test a Login and Logout functionality

Scenario 1:

Unsuccessful Login with Invalid Credentials

  1. Given User is on Homepage
  2. When User Navigate to Login Page
  3. And User enters Username and Password
  4. But The user credentials are wrong
  5. Then Message displayed Wrong Username & Password

Scenario 2: 

Successful Login with Valid Credentials

  1. Given User is on Homepage
  2. When User Navigate to Login Page
  3. And User enters Username and Password
  4. Then Message displayed Log in Successfully

Note:

  1. We need to write the “Step definition” in a java class for each and every behavior written in the ‘.feature’ file.
  2. All the web elements are declared in a Class(Objects.java) under “com.cucumber.commonlibs” package.
  3. All the constants are declared in a Class(login.java) under “com.cucumber.stepdefinition” package.
  4. The trigger of execution is declared in a Class(RunCucumberTest.java) under “com.cucumber.stepdefinition” package.

Step Definition:

  • A Step Definition is a small piece of code with a pattern attached to it.
  • In other words, a Step Definition is a java method in a class with an annotation above it. An annotation followed by the pattern is used to link the Step Definition to all the matching Steps.
  • Cucumber finds the “Step Definition” file, with the help of Glue code in Cucumber Options.

Example:

@Then("^Message displayed Login Successfully$")
 public void message_displayed_Login_Successfully() throws Throwable {
 System.out.println("Login Successfully");
 }

Advantages:

  • “Mail notification” enabled, on failure of build.
  • “Scheduled execution” can be performed.
  • Captures the “screen shots” promptly, on failure and embeds with the report.
  • Supports to run the execution, individual/suite wise.
  • OS independent and Browser independent.
  • Easy to understand.
  • Easy to use, highly flexible and above all “Free of Cost”.
  • Provides “micro-reporting”, with the statistics of passed, failed, pending, skipped, undefined and missing steps.

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