%0 Journal Article %1 https://doi.org/10.1002/smr.2450 %A Betka, Maik %A Wagner, Stefan %D 2022 %J Journal of Software: Evolution and Process %K mutation myown code-coverage from:wagnerst iste-ese software-testing iste-se %N 11 %P e2450 %R https://doi.org/10.1002/smr.2450 %T Towards practical application of mutation testing in industry — Traditional versus extreme mutation testing %U https://onlinelibrary.wiley.com/doi/abs/10.1002/smr.2450 %V 34 %X Abstract Mutation testing is a technique that changes code instructions to assess the quality of automated software tests. Industry has not broadly adopted the technique because execution and analysis times are too long and not considered worth the effort. To change this, a variation called “extreme mutation testing” emerged, which mutates whole methods instead of instructions. The extreme variant trades accuracy for speed gains and also provides pre-analyzed results. In this study, we aim to analyze both techniques on their granularity levels, look for benefits when combining them, and find motivations when a developer considers killing mutants. For that, we conducted a case study in a company from the semiconductor industry. We mutated a large Java software project which is tested by more than 11,000 unit tests, analyzed the results, manually inspected more than 1000 mutants, and conducted a focus group with five developers of the software. Among other results, we provide the distribution of traditional across extreme mutants as well as qualitative coding results of our mutant inspection and focus group transcript. We conclude that the traditional approach can be similarly strategically applied as the extreme one and that motivations of developers to target mutants are mostly not code related.

@article{https://doi.org/10.1002/smr.2450, abstract = {Abstract Mutation testing is a technique that changes code instructions to assess the quality of automated software tests. Industry has not broadly adopted the technique because execution and analysis times are too long and not considered worth the effort. To change this, a variation called “extreme mutation testing” emerged, which mutates whole methods instead of instructions. The extreme variant trades accuracy for speed gains and also provides pre-analyzed results. In this study, we aim to analyze both techniques on their granularity levels, look for benefits when combining them, and find motivations when a developer considers killing mutants. For that, we conducted a case study in a company from the semiconductor industry. We mutated a large Java software project which is tested by more than 11,000 unit tests, analyzed the results, manually inspected more than 1000 mutants, and conducted a focus group with five developers of the software. Among other results, we provide the distribution of traditional across extreme mutants as well as qualitative coding results of our mutant inspection and focus group transcript. We conclude that the traditional approach can be similarly strategically applied as the extreme one and that motivations of developers to target mutants are mostly not code related.}, added-at = {2022-12-16T12:45:48.000+0100}, author = {Betka, Maik and Wagner, Stefan}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/25d8c6ebf89fb839008441ac6fbd0a859/iste-se}, doi = {https://doi.org/10.1002/smr.2450}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/smr.2450}, interhash = {f40d78b9a1053e85141a5296d2b159c3}, intrahash = {5d8c6ebf89fb839008441ac6fbd0a859}, journal = {Journal of Software: Evolution and Process}, keywords = {mutation myown code-coverage from:wagnerst iste-ese software-testing iste-se}, number = 11, pages = {e2450}, timestamp = {2022-12-16T11:45:48.000+0100}, title = {Towards practical application of mutation testing in industry — Traditional versus extreme mutation testing}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/smr.2450}, volume = 34, year = 2022 }