Ethical AI-Powered Regression Test Selection
This work tackles ethical problems in software testing for developers and organizations, but it is incremental as it builds on existing AI ethics research.
The paper addresses ethical challenges in AI-powered regression test selection (AI-RTS), identifying three key issues (responsibility assignment, bias, and lack of participation) and proposing three approaches (explicability, supervision, diversity) along with a checklist for stakeholders.
Test automation is common in software development; often one tests repeatedly to identify regressions. If the amount of test cases is large, one may select a subset and only use the most important test cases. The regression test selection (RTS) could be automated and enhanced with Artificial Intelligence (AI-RTS). This however could introduce ethical challenges. While such challenges in AI are in general well studied, there is a gap with respect to ethical AI-RTS. By exploring the literature and learning from our experiences of developing an industry AI-RTS tool, we contribute to the literature by identifying three challenges (assigning responsibility, bias in decision-making and lack of participation) and three approaches (explicability, supervision and diversity). Additionally, we provide a checklist for ethical AI-RTS to help guide the decision-making of the stakeholders involved in the process.