Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction Strategies

TL;DR

Sentinel is a hyper-heuristic that automatically generates optimal mutation cost reduction strategies for software testing, significantly outperforming existing methods across numerous real-world software versions.

Contribution

The paper introduces Sentinel, a novel multi-objective evolutionary hyper-heuristic that automates mutation strategy generation, eliminating manual configuration for different software under test.

Findings

Sentinel-generated strategies outperform baseline strategies in 95% of cases.

Sentinel strategies are statistically better than state-of-the-art in 88% of cases.

Strategies for one software version remain effective in 95% of subsequent versions.

Abstract

Mutation testing is an effective approach to evaluate and strengthen software test suites, but its adoption is currently limited by the mutants' execution computational cost. Several strategies have been proposed to reduce this cost (a.k.a. mutation cost reduction strategies), however none of them has proven to be effective for all scenarios since they often need an ad-hoc manual selection and configuration depending on the software under test (SUT). In this paper, we propose a novel multi-objective evolutionary hyper-heuristic approach, dubbed Sentinel, to automate the generation of optimal cost reduction strategies for every new SUT. We evaluate Sentinel by carrying out a thorough empirical study involving 40 releases of 10 open-source real-world software systems and both baseline and state-of-the-art strategies as a benchmark. We execute a total of 4,800 experiments, and evaluate their results with both quality indicators and statistical significance tests, following the most recent best practice in the literature. The results show that strategies generated by Sentinel outperform the baseline strategies in 95% of the cases always with large effect sizes. They also obtain statistically significantly better results than state-of-the-art strategies in 88% of the cases, with large effect sizes for 95% of them. Also, our study reveals that the mutation strategies generated by Sentinel for a given software version can be used without any loss in quality for subsequently developed versions in 95% of the cases. These results show that Sentinel is able to automatically generate mutation strategies that reduce mutation testing cost without affecting its testing effectiveness (i.e. mutation score), thus taking off from the tester's shoulders the burden of manually selecting and configuring strategies for each SUT.