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

Vincenzo Musco 1, 2, 3, 4 Martin Monperrus 1, 2 Philippe Preux 3, 4
1 SPIRALS - Self-adaptation for distributed services and large software systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
4 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
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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Submitted on : Monday, July 18, 2016 - 6:19:03 PM
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Vincenzo Musco, Martin Monperrus, Philippe Preux. A Large-scale Study of Call Graph-based Impact Prediction using Mutation Testing. Software Quality Journal, Springer Verlag, 2017, 25 (3), pp.921-950. ⟨10.1007/s11219-016-9332-8⟩. ⟨hal-01346046⟩

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