This paper provides the results of a survey of the grey literature concerning the use of artificial intelligence to improve test automation practices. We surveyed more than 1, 200 sources of grey literature (e.g., blogs, white-papers, user manuals, StackOverflow posts) looking for highlights by professionals on how AI is adopted to aid the development and evolution of test code. Ultimately, we filtered 136 relevant documents from which we extracted a taxonomy of problems that AI aims to tackle, along with a taxonomy of AI-enabled solutions to such problems. Manual code development and automated test generation are the most cited problem and solution, respectively. The paper concludes by distilling the six most prevalent tools on the market, along with think-aloud reflections about the current and future status of artificial intelligence for test automation.
AI-based test automation: A grey literature analysis
Ricca F.;
2021-01-01
Abstract
This paper provides the results of a survey of the grey literature concerning the use of artificial intelligence to improve test automation practices. We surveyed more than 1, 200 sources of grey literature (e.g., blogs, white-papers, user manuals, StackOverflow posts) looking for highlights by professionals on how AI is adopted to aid the development and evolution of test code. Ultimately, we filtered 136 relevant documents from which we extracted a taxonomy of problems that AI aims to tackle, along with a taxonomy of AI-enabled solutions to such problems. Manual code development and automated test generation are the most cited problem and solution, respectively. The paper concludes by distilling the six most prevalent tools on the market, along with think-aloud reflections about the current and future status of artificial intelligence for test automation.
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