Towards an automated approach for bug fix ...
Document type :
Communication dans un congrès avec actes
Title :
Towards an automated approach for bug fix pattern detection
Author(s) :
Madeiral, Fernanda [Auteur]
Federal University of Uberlândia [Uberlândia] [UFU]
Durieux, Thomas [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Sobreira, Victor [Auteur]
Federal University of Uberlândia [Uberlândia] [UFU]
Maia, Marcelo [Auteur]
Federal University of Uberlândia [Uberlândia] [UFU]
Federal University of Uberlândia [Uberlândia] [UFU]
Durieux, Thomas [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Sobreira, Victor [Auteur]
Federal University of Uberlândia [Uberlândia] [UFU]
Maia, Marcelo [Auteur]
Federal University of Uberlândia [Uberlândia] [UFU]
Conference title :
VEM '18 - Proceedings of the VI Workshop on Software Visualization, Evolution and Maintenance
City :
São Carlos
Country :
Brésil
Start date of the conference :
2018-09-19
Publication date :
2018
HAL domain(s) :
Informatique [cs]/Génie logiciel [cs.SE]
English abstract : [en]
The characterization of bug datasets is essential to support the evaluation of automatic program repair tools. In a previous work, we manually studied almost 400 human-written patches (bug fixes) from the Defects4J dataset ...
Show more >The characterization of bug datasets is essential to support the evaluation of automatic program repair tools. In a previous work, we manually studied almost 400 human-written patches (bug fixes) from the Defects4J dataset and annotated them with properties, such as repair patterns. However, manually finding these patterns in different datasets is tedious and time-consuming. To address this activity, we designed and implemented PPD, a detector of repair patterns in patches, which performs source code change analysis at abstract-syntax tree level. In this paper, we report on PPD and its evaluation on Defects4J, where we compare the results from the automated detection with the results from the previous manual analysis. We found that PPD has overall precision of 91% and overall recall of 92%, and we conclude that PPD has the potential to detect as many repair patterns as human manual analysis.Show less >
Show more >The characterization of bug datasets is essential to support the evaluation of automatic program repair tools. In a previous work, we manually studied almost 400 human-written patches (bug fixes) from the Defects4J dataset and annotated them with properties, such as repair patterns. However, manually finding these patterns in different datasets is tedious and time-consuming. To address this activity, we designed and implemented PPD, a detector of repair patterns in patches, which performs source code change analysis at abstract-syntax tree level. In this paper, we report on PPD and its evaluation on Defects4J, where we compare the results from the automated detection with the results from the previous manual analysis. We found that PPD has overall precision of 91% and overall recall of 92%, and we conclude that PPD has the potential to detect as many repair patterns as human manual analysis.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Nationale
Popular science :
Non
Collections :
Source :
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- http://arxiv.org/pdf/1807.11286
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- 1807.11286
- Open access
- Access the document