Reference : A Closer Look at Real-World Patches
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/36571
A Closer Look at Real-World Patches
English
[en] A Closer Look at Real-World Patches
Liu, Kui mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Kim, Dongsun mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Koyuncu, Anil mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Li, Li mailto [Monash University > Faculty of Information Technology]
Bissyande, Tegawendé François D Assise mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Le Traon, Yves mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Sep-2018
12
Bug fixing is a time-consuming and tedious task. To reduce the manual efforts in bug fixing, researchers have presented automated approaches to software repair. Unfortunately, recent studies have shown that the state-of-the-art techniques in automated repair tend to generate patches only for a small number of bugs even with quality issues (e.g., incorrect behavior and nonsensical changes). To improve automated program repair (APR) techniques, the community should deepen its knowledge on repair actions from real-world patches since most of the techniques rely on patches written by human developers. Previous investigations on real-world patches are limited to statement level that is not sufficiently fine-grained to build this knowledge. In this work, we contribute to building this knowledge via a systematic and fine-grained study of 16,450 bug fix commits from seven Java open-source projects. We find that there are opportunities for APR techniques to improve their effectiveness by looking at code elements that have not yet been investigated. We also discuss nine insights into tuning automated repair tools. For example, a small number of statement and expression types are recurrently impacted by real-world patches, and expression-level granularity could reduce search space of finding fix ingredients, where previous studies never explored.
Yes
International
34th IEEE International Conference on Software Maintenance and Evolution
from 23-09-2018 to 29-09-2018
IEEE
Madrid
Spain
[en] Program patch ; fix pattern ; abstract syntax tree
[en] Bug fixing is a time-consuming and tedious task. To reduce the manual efforts in bug fixing, researchers have presented automated approaches to software repair. Unfortunately, recent studies have shown that the state-of-the-art techniques in automated repair tend to generate patches only for a small number of bugs even with quality issues (e.g., incorrect behavior and nonsensical changes). To improve automated program repair (APR) techniques, the community should deepen its knowledge on repair actions from real-world patches since most of the techniques rely on patches written by human developers. Previous investigations on real-world patches are limited to statement level that is not sufficiently fine-grained to build this knowledge. In this work, we contribute to building this knowledge via a systematic and fine-grained study of 16,450 bug fix commits from seven Java open-source projects. We find that there are opportunities for APR techniques to improve their effectiveness by looking at code elements that have not yet been investigated. We also discuss nine insights into tuning automated repair tools. For example, a small number of statement and expression types are recurrently impacted by real-world patches, and expression-level granularity could reduce search space of finding fix ingredients, where previous studies never explored.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Security Design and Validation Research Group (SerVal)
Fonds National de la Recherche - FnR
http://hdl.handle.net/10993/36571

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