Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness

1 code implementation • 8 Aug 2022 • Haoye Tian, Xunzhu Tang, Andrew Habib, Shangwen Wang, Kui Liu, Xin Xia, Jacques Klein, Tegawendé F. Bissyandé

To tackle this problem, our intuition is that natural language processing can provide the necessary representations and models for assessing the semantic correlation between a bug (question) and a patch (answer).

Question Answering

MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning

1 code implementation • 13 Jun 2022 • Weiguo Pian, Hanyu Peng, Xunzhu Tang, Tiezhu Sun, Haoye Tian, Andrew Habib, Jacques Klein, Tegawendé F. Bissyandé

Representation learning of source code is essential for applying machine learning to software engineering tasks.

Code Completion Code Summarization +2

Predicting Patch Correctness Based on the Similarity of Failing Test Cases

1 code implementation • 28 Jul 2021 • Haoye Tian, Yinghua Li, Weiguo Pian, Abdoul Kader Kaboré, Kui Liu, Andrew Habib, Jacques Klein, Tegawendé F. Bissyande

Then, after collecting a large dataset of 1278 plausible patches (written by developers or generated by some 32 APR tools), we use BATS to predict correctness: BATS achieves an AUC between 0. 557 to 0. 718 and a recall between 0. 562 and 0. 854 in identifying correct patches.

Representation Learning

Neural Bug Finding: A Study of Opportunities and Challenges

no code implementations • 1 Jun 2019 • Andrew Habib, Michael Pradel

Static analysis is one of the most widely adopted techniques to find software bugs before code is put in production.