Archive
Blog - posts for August 2021
Aug 13 2021
SWForum DECODER campaign
Date: 13 August 2021
Place: online
Link to SWForum DECODER
The campaign invites researchers, developers and testers to try two DECODER open source software tools - JMLgen and Doc2Json - and get rewarded for their efforts.
SWForum.eu works to enhance the visibility and increase the competitiveness of research and innovation in several fields including software technologies, digital infrastructure, cybersecurity, and standards, especially European funded Research and Innovation Action (RIA) projects. Moreover, the project aims to introduce best practices and technology transfer opportunities to cross-synergise European excellence.
Aug 10 2021
Automated Classification of Overfitting Patches with Statically Extracted Code
Authors: He Ye; Jian Gu; Matias Martinez; Thomas Durieux; Martin Monperrus
Contact: heye@kth.se - KTH Royal Institute of Technology School of Computer Science and Communication, 156318 Stockholm, Stockholm, Sweden
Publication: IEEE Transactions on Software Engineering - Article link
Abstract:
Automatic program repair (APR) aims to reduce the cost of manually fixing software defects. However, APR suffers from generating a multitude of overfitting patches, those patches that fail to correctly repair the defect beyond making the tests pass. This paper presents a novel overfitting patch detection system called ODS to assess the correctness of APR patches. ODS first statically compares a patched program and a buggy program in order to extract code features at the abstract syntax tree (AST) level. Then, ODS uses supervised learning with the captured code features and patch correctness labels to automatically learn a probabilistic model. The learned ODS model can then finally be applied to classify new and unseen program repair patches. We conduct a large-scale experiment to evaluate the effectiveness of ODS on patch correctness classification based on 10,302 patches from Defects4J, Bugs.jar and Bears benchmarks. The empirical evaluation shows that ODS is able to correctly classify 71.9% of program repair patches from 26 projects, which improves the state-of-the-art. ODS is applicable in practice and can be employed as a post-processing procedure to classify the patches generated by different APR systems.
Ref: H. Ye, J. Gu, M. Martinez, T. Durieux and M. Monperrus, "Automated Classification of Overfitting Patches with Statically Extracted Code Features," in IEEE Transactions on Software Engineering, doi: 10.1109/TSE.2021.3071750.