[en] To date, over 40 Automated Program Repair (APR) tools have been designed with varying bug-fixing strategies, which have been demonstrated to have complementary performance in terms of being effective for different bug classes. Intuitively, it should be feasible to improve the overall bug-fixing performance of APR via assembling existing tools. Unfortunately, simply invoking all available APR tools for a given bug can result in unacceptable costs on APR execution as well as on patch validation (via expensive testing). Therefore, while assembling existing tools is appealing, it requires an efficient strategy to reconcile the need to fix more bugs and the requirements for practicality. In light of this problem, we propose a Preference-based Ensemble Program Repair framework (P-EPR), which seeks to effectively rank APR tools for repairing different bugs. P-EPR is the first non-learning-based APR ensemble method that is novel in its exploitation of repair patterns as a major source of knowledge for ranking APR tools and its reliance on a dynamic update strategy that enables it to immediately exploit and benefit from newly derived repair results. Experimental results show that P-EPR outperforms existing strategies significantly both in flexibility and effectiveness.
Practical Program Repair via Preference-based Ensemble Strategy
Date de publication/diffusion :
14 avril 2024
Nom de la manifestation :
International Conference on Software Engineering (ICSE 2024)
Lieu de la manifestation :
Lisbon, Portugal
Date de la manifestation :
14-20 April, 2024
Numéro de la conférence :
46
Manifestation à portée :
International
Titre de l'ouvrage principal :
ICSE'24: Proceedings of the 46th International Conference on Software Engineering (ICSE 2024)
Maison d'édition :
IEEE, Washington, DC, Etats-Unis
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
Projet européen :
H2020 - 949014 - NATURAL - Natural Program Repair
Organisme subsidiant :
Union Européenne
Subventionnement (détails) :
This research was partially supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 949014)
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