A tester’s thoughts on Automation and AI: 4

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The amount of available data, i.e. information, is increasing. It can be user behaviour, bug reports from testing, bug reports from production, test cases, errors from logging, results from automated tests, response times, CPU usage and more. This information can be used to improve the development and the testing. The challenge is the sheer amount of data and the time it would take us to analyze it all. This is where we can benefit from using IT to help with the task. By using analytics and complement it with learning capabilities (AI) we can get help to use all this information to improve.

When we’ve analyzed the information in the bug reports, we’ve always found interesting facts that helped us to improve. We’ve worked with having displays that tell us the status in production when it comes to response times, error in logs and if integrations are alive. All this has been beneficial. What often stops us from leveraging the data to its’ full potential, is the amount of time needed to properly analyze the data, so we can act upon it. Therefore, I’m looking forward to the development of analytics and AI to support the testing, i.e. Cognitive QA.

Eva Holmquist

About

Eva Holmquist has more than twenty-eight years of professional IT experience, working as a programmer, project manager and at every level of the testing hierarchy from a tester through test manager. She has also worked with test process improvements and in test education as a teacher and with the development of courses including a Swedish ISTQB Foundation certification course. Author of the book ”Praktisk mjukvarutestning” (Software Testing in Practice) as well as science fiction and fantasy novels. Eva works as a Senior Test Specialist at Sogeti helping clients improve their testing practices using her broad experience in system development, process improvements, and education. She is a frequent speaker and has during the last year held presentations about agile testing, DevOps and quality assurance, cognitive quality assurance and bias in artificial intelligence.

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