A Large-scale Study of Call Graph-based ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
A Large-scale Study of Call Graph-based Impact Prediction using Mutation Testing
Auteur(s) :
Musco, Vincenzo [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Université de Lille, Sciences et Technologies
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Monperrus, Martin [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Université de Lille, Sciences et Technologies
Preux, Philippe [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Self-adaptation for distributed services and large software systems [SPIRALS]
Université de Lille, Sciences et Technologies
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Monperrus, Martin [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Université de Lille, Sciences et Technologies
Preux, Philippe [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Titre de la revue :
Software Quality Journal
Pagination :
921–950
Éditeur :
Springer Verlag
Date de publication :
2017-09
ISSN :
0963-9314
Mot(s)-clé(s) en anglais :
Mutation Testing
Change Impact Analysis
Call Graphs
Change Impact Analysis
Call Graphs
Discipline(s) HAL :
Informatique [cs]
Informatique [cs]/Génie logiciel [cs.SE]
Informatique [cs]/Génie logiciel [cs.SE]
Résumé en anglais : [en]
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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
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