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Mutation analysis assesses a test suites adequacy by measuring its ability to detect small artificial faults, systematically seeded into the tested program. Mutation analysis is considered one of the strongest test-adequacy criteria. Mutation testing builds on top of mutation analysis and is a testing technique that uses mutants as test goals to create or improve a test suite. Mutation testing has long been considered intractable because the sheer number of mutants that can be created represents an insurmountable problemboth in terms of human and computational effort. This has hindered the adoption of mutation testing as an industry standard. For example, Google has a codebase of two billion lines of code and more than 150,000,000 tests are executed on a daily basis. The traditional approach to mutation testing does not scale to such an environment; even existing solutions to speed up mutation analysis are insufficient to make it computationally feasible at such a scale. To address these challenges, this paper presents a scalable approach to mutation testing based on the following main ideas: (1) mutation testing is done incrementally, mutating only changed code during code review, rather than the entire code base; (2) mutants are filtered, removing mutants that are likely to be irrelevant to developers, and limiting the number of mutants per line and per code review process; (3) mutants are selected based on the historical performance of mutation operators, further eliminating irrelevant mutants and improving mutant quality. This paper empirically validates the proposed approach by analyzing its effectiveness in a code-review-based setting, used by more than 24,000 developers on more than 1,000 projects. The results show that the proposed approach produces orders of magnitude fewer mutants and that context-based mutant filtering and selection improve mutant quality and actionability. Overall, the proposed approach represents a mutation testing framework that seamlessly integrates into the software development workflow and is applicable to industrial settings of any size.

Various proxy metrics for test quality have been defined in order to guide developers when writing tests. Code coverage is particularly well established in practice, even though the question of how coverage relates to test quality is a matter of ongoing debate. Mutation testing offers a promising alternative: Artificial defects can identify holes in a test suite, and thus provide concrete suggestions for additional tests. Despite the obvious advantages of mutation testing, it is not yet well established in practice. Until recently, mutation testing tools and techniques simply did not scale to complex systems. Although they now do scale, a remaining obstacle is lack of evidence that writing tests for mutants actually improves test quality. In this paper we aim to fill this gap: By analyzing a large dataset of almost 15 million mutants, we investigate how these mutants influenced developers over time, and how these mutants relate to real faults. Our analyses suggest that developers using mutation testing write more tests, and actively improve their test suites with high quality tests such that fewer mutants remain. By analyzing a dataset of past fixes of real high-priority faults, our analyses further provide evidence that mutants are indeed coupled with real faults. In other words, had mutation testing been used for the changes introducing the faults, it would have reported a live mutant that could have prevented the bug., Comment: To be published in the Proceedings of the International Conference on Software Engineering (ICSE'21)

Mutation testing assesses test suite efficacy by inserting small faults into programs and measuring the ability of the test suite to detect them. It is widely considered the strongest test criterion in terms of finding the most faults and it subsumes a number of other coverage criteria. Traditional mutation analysis is computationally prohibitive which hinders its adoption as an industry standard. In order to alleviate the computational issues, we present a diff-based probabilistic approach to mutation analysis that drastically reduces the number of mutants by omitting lines of code without statement coverage and lines that are determined to be uninteresting - we dub these arid lines. Furthermore, by reducing the number of mutants and carefully selecting only the most interesting ones we make it easier for humans to understand and evaluate the result of mutation analysis. We propose a heuristic for judging whether a node is arid or not, conditioned on the programming language. We focus on a code-review based approach and consider the effects of surfacing mutation results on developer attention. The described system is used by 6,000 engineers in Google on all code changes they author or review, affecting in total more than 13,000 code authors as part of the mandatory code review process. The system processes about 30% of all diffs across Google that have statement coverage calculated. About 15% of coverage statement calculations fail across Google.