Best practices to follow when performing Dynamics upgrade testing:

Plan Ahead:

Before commencing the upgrade process, it is crucial to carefully plan the testing approach. This includes developing a comprehensive test plan that encompasses all the scenarios that need to be tested, including both functional and non-functional aspects. This will guarantee adequate coverage of all critical areas of the system during the testing phase.

It is also important to prioritize the test approach to avoid insufficient or excessive testing. Therefore, it is advisable to consult with the technical expert from the Engineering team initially. The engineer can provide recommendations on any necessary additions or exclusions to the test scope and help finalize the test plan.

Test in a Sandbox Environment:

It is always a good practice to test upgrades in a separate sandbox environment that mimics the production environment. This provides a safe and secure environment to thoroughly test the upgraded system’s features and functionalities. It ensures that any issues discovered during testing do not have an impact on the live system. Furthermore, it allows for multiple iterations of testing without jeopardising data integrity or causing downtime.

Test Data Migration:

If data migration is a part of the upgrade process, it becomes crucial to thoroughly test the migration process itself. It is imperative to ensure that all data is transferred correctly, and that data integrity is maintained throughout the process. This entails verifying that the migrated data appears as expected in the new system and aligns with the intended outcomes.

By conducting comprehensive tests on the migrated data, you can identify any inconsistencies, errors, or discrepancies that may have occurred during the migration. This level of meticulous testing guarantees a seamless transition and minimizes the risk of data loss or corruption, providing confidence in the integrity and reliability of the upgraded system.

Test Third-Party Integrations:

When upgrading the Dynamics system, it is crucial to thoroughly test any third-party integrations that are in place. This testing should cover both inbound and outbound data transfers and verify that the integrations continue to work after the upgrade.

This involves conducting end-to-end testing scenarios that simulate real-world data exchanges between the Dynamics system and the integrated systems. By executing these tests, any potential compatibility issues, data inconsistencies, or disruptions in the information flow can be identified and addressed promptly.

By thoroughly testing third-party integrations, the upgraded Dynamics system can maintain robustness, reliability, and seamlessness in its operation.

Conduct Regression Testing:

Regression testing is a vital component of the upgrade process as it aims to verify that the upgraded Dynamics system does not introduce any new issues or bugs while maintaining the previously working functionality. This type of testing ensures that all the previously working functionality is thoroughly tested to validate its proper functioning in the upgraded environment.

Automated testing tools and frameworks can significantly aid in performing regression testing efficiently by automating repetitive test cases and facilitating test coverage. This approach allows for quick and consistent execution of test cases, reducing the testing effort and providing faster feedback on the system’s functionality.

The importance of regression testing lies in its ability to uncover hidden defects, compatibility issues, or unintended consequences of the upgrade process. By identifying and addressing these issues early on, you can mitigate risks, prevent system disruptions, and maintain a high level of user satisfaction.

Overall, regression testing serves as a critical quality assurance measure to validate the stability and correctness of the upgraded Dynamics system. It provides confidence that the system continues to function as expected, preserving the existing functionality, and ensuring a smooth transition for end-users.

Conduct Performance Testing:

Performance testing is critical, especially if the new version of Dynamics introduces changes that may impact system performance. Test the system under varying loads to identify any performance issues and ensure that the system can handle the expected workload without degradation in its performance.

By identifying and addressing performance issues early on, we can optimise the system’s performance, enhance the user experience, and maintain productivity.

Document Test Results:

It’s essential to document all testing activities and results. This documentation can be used as a reference for future upgrades and to ensure that all areas of the system have been adequately tested.

Strategies for conducting regression testing during a Dynamics upgrade:

Dynamics upgrades can be difficult and time-consuming, but regression testing is essential to making sure everything functions as it should. Regression testing allows us to verify that the Dynamics system’s essential features and workflows have not been jeopardised or badly impacted by the upgrade. By enabling seamless functioning for end users, this helps to preserve the system’s dependability, stability, and usability.

Regression testing during a Dynamics upgrade can be done in the following ways:

Prioritize Testing – Not all functionality in the Dynamics system is equal, and some features may be more critical than others. Therefore, it’s essential to prioritize the testing of critical functionality during the regression testing phase. Focus on the most frequently used features, Screens & Ribbon buttons that use JavaScript extensively, and any areas that have been impacted by the upgrade. Identify high-risk areas that are more prone to issues due to the upgrade and allocate more testing resources to those specific components. This approach ensures that the most critical and vulnerable parts of the system receive the necessary attention during regression testing. By prioritizing based on risk, we can ensure that any potential issues are addressed first.

Create a comprehensive Regression Test Suite – Develop a comprehensive set of test cases specifically designed for regression testing. These test cases should cover all essential functionalities, workflows, and integration touch points within the Dynamics system. The test suite should encompass both positive and negative scenarios to thoroughly validate the system’s behaviour after the upgrade. Additionally, it should include verification of error messages, warnings, pop-ups, and scroll bar functions. These verifications should be conducted in one area, ensuring that they work similarly in other areas as well.

Leverage Automated Testing Tools – Utilize automated testing tools to streamline the regression testing process. Automated tests can significantly speed up the execution of test cases and help identify any unexpected issues efficiently. Additionally, they allow for easier retesting whenever new changes or updates are made to the system. With reusable test scripts, we can establish a strong foundation for ongoing regression testing throughout the lifecycle of the Dynamics system. This saves time and effort by eliminating the need to recreate test cases from scratch.

Test Integrations – Dynamics systems often integrate with other software, such as financial systems, marketing automation software, or customer relationship management tools. When upgrading Dynamics, it’s important to test these integrations thoroughly to ensure that they still function correctly.

Document and Track Defects – Document and report all issues that are identified during regression testing. This helps ensure that issues are tracked and addressed before the system goes live. Use a formal bug-tracking system to manage issues and ensure that they are addressed in a timely manner.

Consider using a Phased Approach – A phased approach to regression testing can help ensure that all functionality is tested thoroughly. This involves testing the most critical functionality first and gradually expanding testing to cover less critical functionality. This approach can help identify and address issues early in the testing process.

Involve Key Stakeholders – End-users can provide valuable feedback during regression testing, as they can identify issues that may not be apparent to testers. Consider involving end-users in the testing process, or conducting user acceptance testing (UAT), to ensure that the system meets their needs. Their input and feedback can provide valuable insights into the expected behaviour of the system and help identify any deviations or issues that may arise during the upgrade.

Use Version Control – Version control is essential during a Dynamics upgrade to ensure that changes are tracked, and that the system can be rolled back if necessary. Use version control software to track changes and ensure that all testing is performed on the correct version of the system.

Establish a Testing Timeline – It’s important to establish a testing timeline and ensure that all testing is completed before the system goes live. This includes allowing time for testing, bug fixing, and retesting. The testing timeline should be communicated to all stakeholders to ensure that everyone is aware of the testing schedule.

Use a Testing Checklist – A testing checklist can help ensure that all testing is performed consistently and that nothing is missed. The checklist should include all test cases and scenarios that need to be tested, as well as any issues that have been identified and need to be addressed.

In conclusion, Dynamics upgrade testing can be challenging, but organizations can overcome common challenges with the right approach and ensure a successful upgrade. Regression testing is an important part of the Dynamics upgrade process, and there are several strategies that organizations can use to conduct effective regression testing. By testing integrations, using a phased approach, involving end-users, establishing a testing timeline, and using a testing checklist, organizations can ensure that their Dynamics upgrade is successful and that the system functions correctly. By investing time and resources in regression testing, organizations can minimize the risk of issues arising after the system goes live and ensure that the upgrade is a success.

The post Dynamics upgrade Testing – Best Practices and Strategies for conducting Regression appeared first on Indium.

Azure DevOps is a modern-day tool used for version control and trouble-free team management. An individual can manage his entire team with a minimum of a browser as a requirement. The team can easily be part of different countries and manage their activities without any delay. The development team can be in one country, and the testing team can be working in another country. Even If the management has trust issues, the Azure Audit does an audit of each activity that the team is doing, and the management must only worry about cross-country chatting and financial management.

The following types of testing are mission-critical for ensuring the success and reliability of your software:

1. Unit Testing with Azure DevOps
2. Integration Testing with Azure DevOps
3. System Testing with Azure DevOps
4. Functional Testing with Azure DevOps
5. Acceptance Testing with Azure DevOps
6. Smoke Testing with Azure DevOps
7. Regression Testing with Azure DevOps
8. Performance Testing with Azure DevOps
9. Security Testing with Azure DevOps
10. User Acceptance Testing with Azure DevOps

1. Unit Testing with Azure DevOps

Unit testing is breaking the code into its parts and testing each separate code one by one. This testing technique should never be confused with any other testing technique. This is because unit testing is like laying a brick. Brick by brick, the developer will lay code and test each code unit; this is what unit testing is all about. Azure DevOps creates version-controlled parts of the project; they can be assigned, and the automation tests can be tested using a version-controlled build. It also provides the user with the ability to have a view based on recent pipeline activities and control access to various stakeholders.

Fig 1. Pipelines in Azure DevOps

2. Integration Testing with Azure DevOps

Integration testing establishes testing techniques for when the individual bricks of code are laid together to test the data movement and failure points when separate developers merge their code together. Since each developer is separate, they can make fatal flaws in how the code blends together. Terraform is a recommended tool from the Azure team for such chaotic activity. The tool allows the user to create their own customised configuration files and allows the developer or tester to test the ability of their code to work with these config files, along with an additional static code analysis feature. For more information regarding Terraform, visit their website, Terraform by HashiCorp. Another cool thing about Terraform is that it is Datadog-ready.

Fig 2. Integration Testing flow using Terraform

3. System Testing with Azure DevOps

System testing tests all the modules together and is closely related to integration testing in the sense that all modules are integrated together to do a full system QA. Azure DevOps allows integration with various service providers. The workings of this are already mentioned in integration testing. Another nuance of system testing is that the testers may not have the necessary understanding of how the code works. It is divided into functional and non-functional testing.

Also Read: Testing Assistive Technologies in a Product

4. Functional Testing with Azure DevOps

Testers like to fondly call functional testing feature testing because it’s exactly that. The tester tests all the features of the individual module and sees that the features that you intended to have in the software are there in the product. A few years of experience will tell you that Azure DevOps is a lifesaver in terms of linking manual test cases with bugs, PBIs, and feature requests. The Azure DevOps and its rich UI provide a very good mapping of individual features of the product, which allows newbies to join the team to understand the pros and cons of the product within a 60-day period for the development or testing team. Some of these details can be used again by automation to conduct regression testing.

5. Acceptance Testing with Azure DevOps

Code must be accepted in the context of business, user requirements, regulation, the vision of the developers, and feedback from the testers. Just like the Azure DevOps functional test. Azure DevOps is good at keeping track of users’ use cases, scenarios, and even edge cases. Every idea from every individual on the team can be tracked and used at any phase of the project to get a customer-centric product. Standardised tests in the context of regulations that will be applied to the product can also be added to plans in Azure DevOps when they need to be conducted.

6. Smoke Testing with Azure DevOps

This is simply to check or test whether the build is stable enough or worthy enough to do a sanity or regression test. The plan will mostly come from years of experience from previous releases or simply from a list of critical functionalities that should be working, based on the consensus made by the management on what should be working.

7. Regression Testing

Smoke tests and sanity lead to regression with regular intervals to submit a bug. The entire code is under scrutiny. Azure DevOps can help in creating manual tests in a flash based on queries from in-sprint QA and years of experience in testing the product. Azure DevOps helps in creating the test plan and managing it at the time of execution with the help of filters and neat charts that provide feedback to management and the tester about their progress. What shift managers fail to manage in factories, Azure DevOps does in a flash: employee engagement when the work is in progress.

8. Performance Testing

Performance testing is a test to know whether software performs at scale, on a good foundation for speed, and to remove bottlenecks whenever developers and testers identify bottlenecks. The example provided below is based on a tool of preference based on popularity. Ex: JMeter and test engines connected to the virtual machine and various other tools and app services to conduct performance testing using a dashboard from Azure DevOps Performance testing is simple with Azure DevOps.

Fig 3. Load testing flow in Azure DevOps

9. Security Testing

Test the HTML and JavaScript code; for other cases, it’s different, for vulnerability to threats, security loopholes, risks, and emulating an actual hack or attack. Pen testing is one example of security testing. Security testing involves adding common CVE-identifying tools to a Kali Linux machine, adding them to Azure DevOps using Azure agents, running security tests, and providing feedback using Azure charts using data available to Azure DevOps.

10. User Acceptance Testing

Code must be accepted in the context of business and user requirements, as well as based on regulations, by the end users. Support engineers love it because it integrates well with the sales force. Continuous cooperation among support engineers, in-sprint engineers, and regression of manual and automation can constantly happen. They can quickly interact with urgent changes and ensure that the code is stable after testing.

Conclusion

Based on the above description, Azure DevOps is a tool that allows a wide range of integration with tools of maximum importance in the development and testing of a new product. Along with it, it has control over the process of development and testing with neat features like version control based on Git and Team Foundation. Everything is audited. The dev team, management, and product owners can all be in sync with the latest features and details. Since Microsoft owns almost everything related to IDEs, Git, and cloud infrastructure, it is going to be the future of everything in development, at least for the foreseeable future.

The post Empowering Testing Excellence: Exploring the Synergy between Azure DevOps and Diverse Testing Techniques appeared first on Indium.

YES! Agreeing with the fact that everything is made simpler and automatic, from day-to-day activities to our entertainment search with voice recognition. Industries and enterprises are looking for automation of processes that reduce human efforts in repetitive tasks.

Tech people were in search of automation of tasks with human intelligence. Artificial intelligence popped as a solution for automation that mimics human brains. AI has now outperformed in various areas of learning the issues and resolving the bugs. One such realm is AI-based customer service application!

AI is becoming a real game-changer in the conversational customer support services of businesses. NewVoiceMedia has reported that US enterprises are losing around $75 billion per year due to poor customer service. There is a positive impact on adopting AI solutions for businesses in various areas. Financial services have already adopted the conversational AI customer service application to a greater extent.

In this blog post, let’s see how we created an integrated QA model for AI-based customer service application.

How is AI changing the world?- A flash!

As of now, there is no modern AI implemented in sectors. Narrow AI is hitting hard in many industries and is applied in a wide range of data training models and machine learning concepts. Many businesses have adopted AI in various applications such as chatbots, eCommerce, healthcare, customer service, human resource management, crypto trading, crypto mining, workplace communication, and much more.

Soon, the most advanced AI would be monitoring patient records with IoT sensors and prevent malicious health issues in the upcoming era! However, there is a big challenge in testing AI-based applications.

AI automatically collects the data and mimics human intelligence to process further. Hence, it’s hard to build an automated testing solution to detect the behavior of AI applications. You need the help of technical experts who can understand the AI application behavior and automate the testing suite.

Now, let’s get into our client’s business challenges on AI-based customer service applications!

A short story of our client and their reason behind partnering with us!

Our Client served as a leading digital customer experience platform rendering solutions for enterprises with conversational service automation.

They develop customer service solutions using speech recognition, voice assistants, and voice biometrics from devices. Their solutions automate customer service agent interactions, real-time conversation analytics, after-call workarounds, and customer response management.

The conversational customer service application is compatible with deploying in on-premises and cloud environments as per the customer requirements.

Our client solution provides voice and data solutions with speech recognition and voice biometrics to host automated customer service applications. The application is built with the tech stack of Angular and Java, which is available on the cloud platform.

Enter Indium

The AI-based customer service application required an external QA partner with efficient strategies. Indium being a thought leader in Quality Assurance helped validate, capture the full-functional integrity and response to usability. In every software development cycle, the client required quality assurance to ensure efficient customer experience.

Now, let’s look at our client faced business challenges and the solutions offered by our team!

How Indium rendered a test suite that helps quality assurance of AI-based customer service application at every phase?

Our client was continuously going through the software development cycle process of development, testing, and maintenance to enhance the customer experience. In every phase, their in-house QA team was struggling to automate the test suite and optimize the test coverage.

Indium offered automated testing solutions with regression testing and wide test coverage with associated workflows, conversation triggers.

Now, let’s dive in to know about the client’s QA challenges and our solutions in detail!

Development

The in-house QA team had to go through manual or smoke testing after every patch development to ensure high-level quality checks in the application. AI-based customer service application collects data and updates their services. Hence, they were in the push to schedule monthly patches and releases to continuously support quality assurance.

Solution

Indium rendered unit testing solutions powered by DevOps code and adoptive shift learning. These solutions established standards in the codebase and accelerated the development cycle.

We also built the hyper-iterative test design specially designed for AI-based applications. These solutions upgrades were on-demand with AI learning, features, knowledge, and test cases.

Testing

The QA team built a rigid Java-Selenium-based framework for testing the application suite. But, it was not compatible with automating UI-based testing. The in-house team was not ready with automation or test cycle optimization.

Solution

We built a test design to execute various input-output combinations for the business requirements and validations of the application’s reliability to error handling scenarios. The test strategy also covered test cases for potential response flows and usability parameters. A test automation platform- uphoriX was developed on open-source frameworks, custom libraries integrated with CI/CD(Azure), and Protractor for supporting angular-based application testing.

Our team also offered an on-going test suite with an automated setup with scheduled iterations for updating test cases, test coverages, and AI learning pace. We efficiently automated 1700+ regression cases that contributed to 80% of test execution efficiency.

Maintenance

The QA team did not address the test case volume, traceability for testing the comprehensive suite of projects. They did not have regular QA reports or fixes, and also, there were no proper alerts for stakeholders.

Solution

Our team rendered a multi-dimensional testing suite that covered multichannel and cross-channel customer experience, domain validations, contextual test cases involving conversational application. We generated QA reports and alerts to be updated through email and SMS for specified stakeholders.

How we deployed our automated test suite in the client application?

Our client was hosting the AI-based customer service application on the cloud, and hence they requested cloud deployment. We built a test suite that is compatible with on-prem/cloud deployment.

Indium’s test suite impact on client’s AI-based service application

Our client was happier with reducing time and efforts employed in regression testing and report generation to a greater extent. The automation test suite helped the in-house QA team focus on complex real-time glitches rather than answering regression cases with customer service.

Now, wrapping up with the highlights of the business impact created by our automated test suite!

• The unit test in the development cycle ensured high standards in code maintenance and reduced regression testing for QA engineers to 10-15%
• Updated test cases & execution to match the continuous need for validating business requirements.
• Dynamic testing with scheduled test design and updates, integrated testing requirements, proactive communication with product user.
• Faster feedback and report generation with 75-80% automation and minimal intervention towards monitoring test suite.
• QA team focuses on in-depth test cases for complex AI-based glitches and takes care of regression cases through an automated suite. Every day around 85%(10 hours) of manual QA report was saved for every 500 test cases.

The post Indium’s Integrated QA Model for AI-Based Customer Service Application – A Success Story appeared first on Indium.

Based on organization size, the small & medium enterprises segment of the Application Testing Services Market is projected to grow at the highest CAGR during the forecast period. – Based on Markets and Markets report on Data Resiliency Market

Today, application’s high performance is lavish but also a basic need for the business.

Those days are gone when one had to apprehend customer/stakeholders about the benefits of performance testing.

Now, organizations are only worried about the growing market change where they lhave to compete.

Additionally, they need to get acclimated with the speed of changes to adjust time-to-market stresses.

Consequently, performance testing services currently turns out to be extremely critical before the application goes live regardless of whether it’s major or minor discharges or dash for an agile based task.

There is no doubt that starting from requirement gathering to production analysis, application performance an utter importance.

Why we need regression performance testing?

Before discussing on continuous regression performance testing, lets discuss why performance testing is so crucial in SDLC.

• Provide realistic prediction before launching the application or before any minor release
• Pre-production performance testing results assist in finding performance hiccups and resolve those bottlenecks before going to production
• Performance benchmarking can be used which supports business continuity
• Assist in Capacity planning

There are also lot of intangible benefits from performance testing like improved brand identity, customer satisfactions.

Generally, performance-testing takes place right before production release and usually there is a time constraint as the project has to go live on time. On contrary to that, now a days, there are application changes which required to become competitive in market.

So, the actuality is performance testing window is always very short and there are many releases and sprints throughout the year.

In a nutshell, early performance testing in SDLC phases covers all of the above concerns and it is like the sooner problems get identified, the sooner it can be fixed.

Performance testing team is required to test the application quickly, repetitively by closely working with the application development team to meet the application’s speed, scalability and stability.

Time for Continuous Regression Performance Testing

The time has come for continuous regression performance testing, this is required now for all projects at the early stages of SDLC, as it will avoid failure and decrease the overall development cost.

Continuous regression performance testing is an ongoing activity. It is not limited to preproduction activity rather can be continued until post-deployment.

This is absolutely required for leveraging flexibility, coverage and effectiveness.
Continuous performance regression testing services always gives confidence ahead of deployment.

It assures end-users for new features and eliminates the potential risk of arising new performance issues.

Advantages of Continuous Performance Regression Testing

• Swift exposure of performance bottlenecks: Executing a performance testing at the initial stages of the product development will help identify bottlenecks, thus fixing the bugs at the initial stages
• Saving time, effort and costs: As the performance bottleneck is uncovered at a primary stage and fixed earlier. It will reduce the resource and time requirements to fix the critical issues in an immense way.
• Fewer Bugs: Any bugs that are left unchecked can lead to further complex errors in the future stages of production. Since the bugs are caught earlier, the complication of the identified bugs will be managed easily and the real number of bugs to be fixed will also be manageable.
• Stabilized Application: Continuous iterations are required in an Agile environment, this will lead to more repeated and severe performance testing, this results in stable applications, and all key risks are taken care of, adding the requisite stability.
• Reusable scripts means saving of effort: There might be different scripts in the execution testing process that might be reused proceeding. This re-utilize may prompt saving reserve funds of up to 60%.
• Boosts Confidence: Since the product gets tried on the entirety of its parameters and for usefulness for a significant number of times on an iterative premise, the organizations certainty and confidence in its item gets reaffirmed and they can make sure of a glittering example of overcoming adversity

In Conclusion

Regression testing capably dives into the examination of coding refreshes and their effect upon application associations with conditions and interfaces. The essential idea of regression testing is to guarantee the product’s capacity to perform proposed capacities continuous by changes or enhancements.

The post Continuous Performance Regression Testing appeared first on Indium.

The term ‘regression’ sounds very tedious, terrifying and too technical, but it’s really not.
Literally, “regression” means “going back” to the previous state of your application just to ensure that any new change, bug fix, patch or version did not disturb the existing code of the application and the app runs as expected.

Browse back to learn our experts have said about Regression Testing

What is different in Mobile Regression Testing?

There is no big difference in regression testing of a mobile application to that of the web application.

This is because no matter what the platform is, the application’s functionality is not going to change.

Example: If the ‘LIKE button in Facebook’ has to work on the web platform, then it should work the same way on the mobile platform.

This means, that the functional aspects of the application don’t make difference in mobile regression to that of web regression testing.

Also, the test cases used for web app regression testing can be used for mobile regression testing.

But, Wait!!!

Then why Mobile Regression Testing services is such a chaos?

There are other aspects which should be considered while executing regression test plan for a mobile application.

They are,

• How does the application perform in mobile interruptions?
• Is the mobile application properly getting installed on every version of mobile devices?
• Is the usability aspect of the mobile application up to user satisfaction?
• How compatible is your application across devices and platforms?
• Is the layout and navigation of the application user-friendly?
• How consistent is the application in portrait and landscape view?

These are only few examples. This list will grow bigger based on the number of devices, platforms, OSs, country, currency involved, etc.

But these issues become traumatic when neglected.

Watch Our iSAFE Automation Framework Demo

Things to Consider for Adopting a Mobile Regression Testing Plan

In a strange way, mobile application testing is dualistic. There is no middle ground. The growing use of smartphones, tablets, and other mobile devices have accelerated mobile applications and mobile application testing indefinitely.

Looking for a Partner to Handle your Mobile QA Needs?

Like any desktop or web application testing, mobile application testing hones in on the quality and performance of the end product.

Mobile Regression Testing Puzzle can be solved easily when the below aspects are followed.

1. Select Mobile phones with features that fits best for your customer’s own usage.
2. Emulation & Simulation helps with fast development of test cases and test automation
3. Testing mobile app on cloud reduces the delivery time and the cost suffered on purchasing real devices.

Outsource Mobile Testing Now to Avoid Mobile Testing Overheads

1. The mobile applications should be validated for Network Connectivity under full network, intermittent network, and no network.
2. Both Manual and regression test automation are required based on the number of test cases, scenarios, and iterations.
3. Perform load and stress testing of your application
4. Perform security testing on the app and check for other vulnerabilities

The post Regression Testing for Mobile Applications – Unveiling the ways for Successful Mobile Automation Testing appeared first on Indium.

• “Is it possible to use Agile methodologies in Automated Regression Testing?”,
• “Can traditional tools be used”?
• “How efficient open-source testing tools are?”
• “What are the challenges of implementing regression test automation in Agile Environment”.

In this article, let us analyze some of the key challenges faced while implementing Regression Test Automation with Agile methodologies. Automated regression testing services in the Agile environment stands a risk of becoming chaotic, unstructured and uncontrolled.

Agile Projects have their own challenges with respect to test automation. Vague project scope, numerous iterations, negligible documentation and early & frequent Automation requests mandate a lot of challenges to the Test Automation Team.

Some of these challenges are discussed below

Challenge 1: Requirement Phase

Regression Test Automation developer captures requirements in the form of “user stories”, which are brief descriptions of customer-oriented functionality.

Each requirement has to be prioritized as follows:

High: The mission critical requirements that are absolute to be considered in the first release

Medium: The requirements which are important but can be worked around until implemented.

Low: These requirements are nice-to-have but not critical to the operation of the software.

The release “iterations” are planned, once priories are established. Normally, each Agile release iteration takes between 1 to 3 months to deliver. Customers/software folks take the liberty to make too many changes to the requirements. Sometimes, these changes prove to be volatile that the iterations are bounced off. These changes are greater challenges while implementing agile regression testing process.

Challenge 2: Selecting Right Testing Tool

Traditional, testing tools with record-and-playback-features force the testing teams to wait until the complete iteration of the software is done.

Additionally, traditional test automation tools don’t work in Agile context because they only solve traditional problems, and those are different from the challenges faced by the Agile Automation teams.

Test Automation in the early stages of an agile project is generally very tough, but as the system grows and evolves, some aspects settle and it becomes appropriate to deploy automation.

So the choice of testing tools becomes critical for reaping the efficiency and quality benefits of agile.

Challenge 3: Script Development

The Automation testers, developers, business analysts and project stakeholders contribute to kick-off meetings where “user-stories” are selected for the next sprint.

Once the “user-stories” are selected then they are used as the basis for a set of tests.

With each iteration, the functionality grows and regression testing must be performed to ensure that existing functionality has not been impacted by the introduction of new functionality in every iteration cycle.

The scale of the regression testing grows with each sprint and ensures that this remains a manageable task the test team to use the test automation for the regression suite.

Challenge 4: Resource Management

The Agile approach requires a mixture of testing skills. The testing resource will be required to define unclear scenarios and test cases, conduct manual testing along with the developers, write automated regression tests and execute the automated regression packages.

As the project progresses, specialist skills will also be required to cover further test areas that might include integration and performance testing services.

There should be an appropriate mix of domain specialist who plan and gather requirements. The challenging part in the Resource management is to find out test resources with multiple skills and allocate them.

Challenge 5: Communication

Good communication must exist among Automation testing team, developers, business analysts, and stakeholders.

There must be a highly collaborative interaction between client and the delivery teams. More client involvement implies more suggestions or changes from the client. It implies more bandwidth for communication.

The key challenge is that the process should be able to capture and effectively implement all the changes and data integrity needs to be retained.

In traditional testing, developers and testers are like oil and water, but in the agile environment, the challenging task is that they both must work together to achieve the target.

The agile team is spread across the globe based on the size of the project. Hence, it is imperative to maintain proper defect tracking and the communication channel to make a successful agile testing process.

Defect tracking tools such as Bugzilla, Jira, Mantis, HP/ALM, BugNet etc. should be used to keep track on the bugs logged.

These tools also help to closely watch if the bugs are getting repeated in the subsequent test cycle. For effective communication channels such as Skype, TeamViewer, SharePoint, Slack, Jive can be used. Besides, proper task allocation, stakeholder’s responsibilities, the creation of proper email distribution list will all help avoid miscommunication.

Challenge 6: Daily Scrum Meeting

Daily Scrum Meeting is one of the key activities in Agile Process. Teams meet for 15 minutes stand up sessions. The effectiveness of these meetings and how far these meetings have helped Automation Practice is always a challenge

Challenge 7: Release Phase

The aim of the Agile project is to deliver a basic working product as quickly as possible and then to go through a process of continual improvement.

This means that there is no single release phase for a product. The challenging part lies in integration testing and acceptance testing of the product.

If all these challenges are addressed in a well-optimized manner, then Automated Regression Testing in the Agile environment is an excellent opportunity for QA to take leadership of the agile processes.

It is better placed to bridge the gap between users and developers, understand both what is required, how it can be achieved and how it can be assured prior to deployment.

Automation practice should have a vested interest in both the how and the result, as well as continuing to assure that the whole evolving system meets business objectives and is fit for purpose.

Source URL: https://www.softwaretestinghelp.com/automated-regression-testing-challenges-in-agile-testing-environment/

The post Automated Regression Testing Challenges in Agile Environment appeared first on Indium.

In this blog, we are going to look into the key difference between these two types of testing. Also, we are going to learn more about the relationship between regression testing and retesting.

So, let’s try to answer the question

What is the difference between Regression Testing and Retesting?

Regression Testing is a category of Software Testing Services performed to make sure the change in the code has not disturbed the current features & functions of the tested application.

Whereas,

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Re-testing is performed to ensure that the test cases that failed the final execution are successfully passed and those failures are fixed.

In the Regression Testing, the test cases are extracted from functional testing to ensure that no new defects are included due to the software update/change & to check whether original features and functionality are working as expected.

Regression testing also ensures that no new defect is introduced to the system. Once the regression test suite is created, the test cases can be automated using automation tool but the same is not applicable for Re-testing.

Jumping into the detailed comparison

In regression testing services, the developer fixes the defect registered by a tester during testing the software application. While in Retesting the same defect is checked to make sure whether the defect is fixed or not. For this they use the stages to reproduce stated in the defect.

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In our earlier blogs on Regression Testing, we discussed in detail about fundamentals of regression testing services, challenges, need, advantage, etc. In this blog, we are going to discuss the top regression testing tools.

Test Automation is a key for regression testing. Manual regression testing tends to get more expensive and time consuming with each new release. Test Automation services always complements the regression test process.

Automated Regression Testing is where one can automate maximum amount of the regression testing efforts. All the earlier performed test cases can be run on new build. In other words, the test cases are set and executing them manually takes time.

Hence, Automating test cases saves a lot of time and is the most efficient regression method. Having said that, the magnitude of regression test automation depends on the amount of test cases that will remain appropriate during the regression cycle time.

If test cases are unpredictable from time to time as application, scope is increasing, then automating the regression procedure will be tedious and time consuming.

Generally, regression-testing tools are of record and playback type. Test cases will be recorded by navigation through the AUT (application under test). It is then verified for expected results.

Regression of GUI Application:

It is difficult to perform GUI regression test when GUI structure is modified. The test cases written on old GUI will become obsolete and are required to be modified.

Reusing the regression test cases means GUI test cases are modified according to new or modified GUI. But this task becomes cumbersome if there are the large set of GUI test cases.

Related:  Read our blog on UI Testing with Ranorex

Listed some of the best regression testing tools

1. QTP
2. Selenium
3. Ranorex
4. Regression Tester
5. vTest
6. Watir
7. Rational Functional Tester
8. SilkTest
9. Winrunner
10. actiWate
11. AdventNet QEngine
12. TimeShiftX

The above mentioned tools are both functional as well as regression testing tools. Adding and bring up-to-date regression test cases into an automation test suite is a tedious task.

Whereas selecting test automation tool for regression tests, it should be checked whether the tool permits to update or add the test cases effortlessly.

Majority of the time, the automated test cases have to be updated due to recurrent changes in the system.

Thanks for reading!!

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We have discussed a lot about the video game market. It is forecasted that the global games market will reach up to $ 108 Billion with a mobile gaming percentage of 42%.

The software with the largest number of tweaks, releases, updates, and version are games. Video games have to be updated in line with the customer sentiments and satisfaction. Thus, Gaming Testing is a segment where regression plays a very crucial role.

Major challenges faced while testing a game software are:

1. Almost all games are based on mathematics, hence a focus should be taken to solve them for testing. It involves a huge amount of time when regression testing proves to be an ice breaking solution.
2. Maintaining deliverables for all the possible game versions, releases and updated is a very difficult task
3. Thinking like an end user of different age category and finding bugs thinking from the user’s perspective is a very difficult task.
4. Time to market is an essence to a game’s success. Testing multiple game version at the same time becomes a very difficult task for the game testing team.

Steps for Regression Testing of a Game Software

Step 1: Smoke and Sanity  Sounds familiar?? But smoke and sanity testing are essential to make sure the system or the platform under test is under desirable conditions. The main purpose is to make sure if the system is steady before the game testing process is originated.

Related:  Read our blog on Smoke Testing vs Sanity Testing

Step 2: Requirements Analysis Testing requirements of the changes or updates to the code need to be meticulously analysed. Frequently users encounter bugs that are originated as a result of last-minute modifications which can be found in requirement analysis. Mandatory requirements for game testing must be cautiously assessed, and the test cases for regression testing services should be organized so that the fundamental features of the game software remain intact.

Step 3: Identifying Test Cases for Critical Game Functions Of the different experiments intended for regression testing, the most important for clients and gamers are the Sanity test cases that check the essential usefulness of the gaming system. In this, regular test cases are then tested on priority. Then, the regression testing test cases are executed, as per the bandwidth and the requirement Integration testing and its test cases are highly significant and there has to be a sequence of regression test cases particularly while executing integration testing. The last moment bug fix, can disrupt the integration between numerous modules, in the tested game applications.

Step 4: Test Case Selection After prioritizing the test cases, the test cases are selected for regression test execution. The choice of these test cases are basically done in the part of frequent changes and based on the criticality and features. Test cases are run rigorously for the particular pieces of code that have suffered numerous changes repetitively.

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