TechArcis – Testing with Analytics, AI, Machine learning and Robotics

A lot of research is happening in these areas and businesses can benefit from Testing use cases such as:

• Data analysis : Using AI and Machine Learning to analyze data from various sources (server logs, defect management tools such as HP ALM and ITSM tools for log data aggregation etc) to identify applications with a history of raising defects.
• UX improvement with sentiment analysis and consumer feedback: Conduct sentiment analysis by extracting trends and analyzing data extracted from app stores (Google Play/Apple Store), social media (Facebook/ Twitter), and from consumer web sites (such as a retailer). Such analysis can help identify issues which can then be addressed through performance testing and usability testing.
• Test case optimization: From identifying redundant test cases to classifying correlated test cases into clusters and executing a few test cases (that are not identical but share similarities and have the same test fail/pass patterns), test execution effort can be reduced significantly.
• Defect prediction for Agile: Using historical data of test cases pass/fail performance for one given sprint to predict a specific sprint’s test pass/fail can help reduce test workload by avoiding testing what doesn’t need to be tested. As Agile and DevOps go main stream with emphasis on CI/CD, AI and machine learning will help automation by letting the tester automate the repetitive parts with more focus directed on the creative, logical and reasoning part that will be significant in the inter-connected world.
• Robotic Testing: From using robots (that can write signatures on a touchscreen, press buttons, hold and swipe multiple cards within a stipulated time) for automating POS testing, to using them for testing handheld control devices and for unattended EMV testing (where they enter cards, punch the PIN code etc), AI and machine learning are helping to automate several time consuming, repetitive tasks.