15 citations · 30 across the 6 of their papers we have counts for
6 papers · 1 filter
Learning to Represent Patches
Xunzhu Tang, Haoye Tian, Zhenghan Chen +6
Patch representation is crucial in automating various software engineering tasks, like determining patch accuracy or summarizing code changes. While recent research has employed de…
Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
Haoye Tian, Xunzhu Tang, Andrew Habib +5
In this work, we propose a novel perspective to the problem of patch correctness assessment: a correct patch implements changes that "answer" to a problem posed by buggy behaviour.…
MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning
Weiguo Pian, Hanyu Peng, Xunzhu Tang +5
Representation learning of source code is essential for applying machine learning to software engineering tasks. Learning code representation from a multilingual source code datase…
The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches
Haoye Tian, Kui Liu, Yinghua Li +7
A large body of the literature on automated program repair develops approaches where patches are automatically generated to be validated against an oracle (e.g., a test suite). Bec…
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
Haoye Tian, Yinghua Li, Weiguo Pian +5
Towards predicting patch correctness in APR, we propose a simple, but novel hypothesis on how the link between the patch behaviour and failing test specifications can be drawn: sim…
Neural Bug Finding: A Study of Opportunities and Challenges
Andrew Habib, Michael Pradel
Static analysis is one of the most widely adopted techniques to find software bugs before code is put in production. Designing and implementing effective and efficient static analy…