A Large-scale Study of Call Graph-based Impact Prediction using Mutation Testing

by Vincenzo Musco, Martin Monperrus and Philippe Preux
Abstract: In software engineering, impact analysis consists in predicting the software elements (e.g. modules, classes, methods) potentially impacted by a change in the source code. Impact analysis is required to optimize the testing effort. In this paper, we propose a framework to predict error propagation. Based on 10 open-source Java projects and 5 classical mutation operators, we create 17000 mutants and study how the error they introduce propagates. This framework enables us to analyze impact prediction based on four types of call graph. Our results show that the sophistication indeed increases completeness of impact prediction. However, and surprisingly to us, the most basic call graph gives the highest trade-off between precision and recall for impact prediction.
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A Large-scale Study of Call Graph-based Impact Prediction using Mutation Testing (, and ), In Software Quality Journal, Springer Verlag, volume 25, .
Vincenzo Musco, Martin Monperrus and Philippe Preux, "A Large-scale Study of Call Graph-based Impact Prediction using Mutation Testing", In Software Quality Journal, Springer Verlag, vol. 25, no. 3, pp. 921–950, 2017.

Bibtex Entry:

@article{musco:hal-01346046,
 title = {{A Large-scale Study of Call Graph-based Impact Prediction using Mutation Testing}},
 author = {Musco, Vincenzo and Monperrus, Martin and Preux, Philippe},
 url = {https://hal.inria.fr/hal-01346046/file/papersqj.pdf},
 journal = {{Software Quality Journal}},
 publisher = {{Springer Verlag}},
 volume = {25},
 number = {3},
 pages = {921--950},
 year = {2017},
 doi = {10.1007/s11219-016-9332-8},
}
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