Résultat de recherche par an
Résultat de recherche par an
Pouria Derakhshanfar, Xavier Devroey, Andy Zaidman, Arie Van Deursen, Annibale Panichella
Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque
Writing a test case reproducing a reported software crash is a common practice to identify the root cause of an anomaly in the software under test. However, this task is usually labor-intensive and time-taking. Hence, evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called Crash Distance to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called MOHO, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-Objectives for crash reproduction, namely test length (to minimize) and method sequence diversity (to maximize), in addition to Crash Distance. We assessed MO-HO using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 non-trivial crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for MO-HO. We evaluated this best-performing algorithm for MO-HO against the state-of-the-art: single-objective approach (Single-Objective Search) and decomposition-based multi-objectivization approach (De-MO). Our results show that MO-HO reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, MO-HO improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-Objective Search and De-MO, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time.
langue originale | Anglais |
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titre | Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020 |
Editeur | ACM Press |
Pages | 211-223 |
Nombre de pages | 13 |
ISBN (Electronique) | 9781450367684 |
Les DOIs | |
Etat de la publication | Publié - sept. 2020 |
Modification externe | Oui |
Evénement | 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020 - Virtual, Melbourne, Australie Durée: 22 sept. 2020 → 25 sept. 2020 |
Nom | Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020 |
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Une conférence | 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020 |
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Pays/Territoire | Australie |
La ville | Virtual, Melbourne |
période | 22/09/20 → 25/09/20 |
Résultats de recherche: Forme non textuelle › Logiciel
Derakhshanfar, P. (Créateur), Devroey, X. (Créateur) & Soltani, M. (Contributeur), Zenodo, 26 avr. 2020
Ensemble de données
Derakhshanfar, P. (Créateur), Devroey, X. (Créateur), Zaidman, A. (Créateur), van Deursen, A. (Créateur) & Panichella, A. (Créateur), Zenodo, 11 août 2020
Ensemble de données