Ethical AI-Powered Regression Test Selection

TL;DR

This paper explores ethical challenges in AI-powered regression test selection, identifying key issues and proposing approaches and a checklist to guide responsible implementation in software testing.

Contribution

It introduces a framework of ethical challenges and solutions for AI-RTS, filling a gap in the literature and providing practical guidance for stakeholders.

Findings

Identified three key ethical challenges in AI-RTS

Proposed three approaches to address ethical issues

Developed a checklist for ethical AI-RTS implementation

Abstract

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.