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In the traditional setup of executing software quality assurance, a lot of data and information are often not considered for further analysis. This shortcoming bears a negative impact on the QA efficiency such that the stability of application can be called to question. The data and information appearing in the form of test results, defect logs, project documentation, and production incidents are voluminous and complex to decipher and analyze. Even test automation as a part of the Agile or DevOps set up cannot be of help here, for it only executes pre-written test scripts iteratively to generate outcomes that may or may not meet the test objectives.

Where typical software test assurance can fail, artificial intelligence and machine learning can chip in to ensure enhanced QA efficiencies and better identification of glitches, defect hotspots, and failure patterns. In fact, the AI-based algorithms can analyze the wealth of information gleaned out of traditional QA software testing and predict a host of possibilities. These can range from offering insights into the stability of application concerned and defect hotspots to enhancing the decision making capability, predicting future defect areas, and incorporating quality in the early part of the SDLC.

How can AI led QA software testing be of help?

• Analyze the historical data and predict the key test parameters for future tests
• Identify duplicate test cases and optimize the test suite
• Execute test cases automatically by identifying the hotspots
• Achieve a better test coverage
• Analyze social media data from customers and offer a comprehensive feedback report detailing the pain areas of customers
• Improve regression testing by identifying the high risk glitch prone areas of an application
• Achieve the development and delivery of a qualitatively superior application
• Deploy applications faster leading to a rich user experience
• Implement with ease on client’s technology platforms

There are a number of AI test automation tools that can be used by the quality assurance software testing services. Some of these are as follows

Testim.io: Specializing in functional and usability testing, Testim.io uses machine learning to execute QA – from authoring to maintenance. The QA testers can use HTML and JavaScript to write algorithms, which can then go on to enhance the quality of software being tested. The tool also helps to increase the stability of test suites.

Appvance: This particular AI-based tool generates test suites to check the usability of an application based on past user behaviour. Since this tool checks the ultimate end user experience, it can play an important role in QA testing services.

Functionize: As evident from its name, the tool based on machine learning can analyze and create multiple tests to execute functional testing.

Test.ai: Useful in carrying out regression testing, the tool identifies the performance metrics and monitors the performance of a mobile application.

Criteria to choose the right tool for AI and software quality assurance testing

Stability and CI/CD integration: In the DevOps software development and testing methodology, there is a need to execute Continuous Improvement/Continuous Delivery of software to achieve user satisfaction. The CI/CD integration also helps in improving the quality of an application. However, predicting the need and areas to be covered for CI/CD integration require an AI-based tool for quicker execution.

Actionable intelligence: The AI tool can analyze a wealth of data and information produced from test reports to generate actionable intelligence for the testers to execute. This dynamic and easily accessible intelligence can come in handy while validating a software in areas such as regression testing, performance, security, functional, and usability.

Easy integration with software development and test tools: The AI tool to be selected for QA testing should be compliant with the software used by testers and developers. The test team should not be forced to learn a new language to operate the AI tool, which otherwise would lead to a delay in test execution. Moreover, the AI tool should not induce a process change while authoring or executing the QA test, for it would then require the test teams to be retrained.

Conclusion

Implementing AI in QA testing can produce better test outcomes at a lesser cost. AI can analyze voluminous test reports and predict the hotspots for glitches. In future, the role of artificial intelligence will transcend testing and cover the development and delivery of software applications.

 This Article is originally published at Medium.com, How to choose the right AI tool for your QA requirements?