Adversarial Patch Generation for Automated Program Repair
Abdulaziz Alhefdhi (1, 2), Hoa Khanh Dam (1), Thanh Le-Cong (3),, Bach Le (3), Aditya Ghose (1) ((1) University of Wollongong, (2) Prince, Sattam bin Abdulaziz University, (3) The University of Melbourne)

TL;DR
This paper introduces NEVERMORE, a learning-based adversarial approach using GANs to generate automated program repairs that closely mimic human fixes, showing promising results on real-world bugs and benchmark datasets.
Contribution
NEVERMORE is the first GAN-based framework for automated program repair trained on historical fixes, improving repair accuracy and scalability over existing methods.
Findings
NEVERMORE matches human fixes for 21.2% of bugs in real-world evaluation.
Successfully repairs 4 bugs in Defects4J unresolved by other methods.
Fixes an additional 8 bugs only solved by some baseline approaches.
Abstract
Automated Program Repair has attracted significant research in recent years, leading to diverse techniques that focus on two main directions: search-based and semantic-based program repair. The former techniques often face challenges due to the vast search space, resulting in difficulties in identifying correct solutions, while the latter approaches are constrained by the capabilities of the underlying semantic analyser, limiting their scalability. In this paper, we propose NEVERMORE, a novel learning-based mechanism inspired by the adversarial nature of bugs and fixes. NEVERMORE is built upon the Generative Adversarial Networks architecture and trained on historical bug fixes to generate repairs that closely mimic human-produced fixes. Our empirical evaluation on 500 real-world bugs demonstrates the effectiveness of NEVERMORE in bug-fixing, generating repairs that match human fixes for…
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Taxonomy
TopicsSoftware Testing and Debugging Techniques · Advanced Malware Detection Techniques · Adversarial Robustness in Machine Learning
