Mutation Testing in Evolving Systems: Studying the relevance of mutants to code evolution

TL;DR

This study investigates commit-relevant mutants in evolving software, showing their distribution outside change areas, the efficiency of selecting relevant mutants, and the limited predictive power of common features, thus enhancing mutation testing during software evolution.

Contribution

It introduces a general definition and experimental approach for identifying commit-relevant mutants, demonstrating their properties and the effectiveness of targeted mutation testing in evolving systems.

Findings

Most commit-relevant mutants are outside changed code areas.

Selective mutation testing can reduce mutants by up to 93%.

Common proxy features do not reliably predict commit-relevant mutants.

Abstract

When software evolves, opportunities for introducing faults appear. Therefore, it is important to test the evolved program behaviors during each evolution cycle. We conduct an exploratory study to investigate the properties of commit-relevant mutants, i.e., the test elements of commit-aware mutation testing, by offering a general definition and an experimental approach to identify them. We thus, aim at investigating the prevalence, location, comparative advantages of commit-aware mutation testing over time (i.e., the program evolution) and the predictive power of several commit-related features to understand the essential properties for its best-effort application case. Our approach utilizes the impact of mutants and the effects of one mutant on another in capturing and analyzing the implicit interactions between the changed and unchanged code parts. The study analyses millions of mutants (over 10 million), 288 commits, five (5) different open-source software projects involving over 68,213 CPU days of computation and sets a ground truth where we perform our analysis. Our analysis shows that commit-relevant mutants are located mainly outside of program commit change (81%), while an effective selection of commit-relevant mutants can reduce the number of mutants by up to 93%. In addition, we demonstrate that commit relevant mutation testing is significantly more effective and efficient than state-of-the-art baselines. Our analysis of the predictive power of mutants and commit-related features in predicting commit-relevant mutants found that most proxy features do not reliably predict commit-relevant mutants. This empirical study highlights the properties of commit-relevant mutants and demonstrates the importance of identifying and selecting commit-relevant mutants when testing evolving software systems.