FixGPT: A Novel Three-Tier Deep Learning Model for Automated Program Repair.

Automatic program repair plays a crucial role in the software development and implementation. While deep learning-based approaches have made significant progress, one inherent challenge is the inefficiency in code representation, which hampers accurate patch generation. Furthermore, the training data used by these data-driven approaches may be limited, and they may not be able to capture the subtle differences between vulnerabilities and patches. To address these issues, FixGPT, we propose a three-tier deep learning model in the study. Specifically, a generative pre-trained transformer model is designed in the first tier to capture code characteristics and programming patterns. The second tier integrates a generation model based on the structure of neural machine translation, for the purpose of generating potential patches. Finally, a contrast model is introduced in the last tier to differentiate between the vulnerability and the patch. We also incorporate the Byte Pair Encoding approach to reduce the search space by converting identifiers into subwords. Detailed experimental studies have been carried out to evaluate the performance of FixGPT on two well-known benchmarking datasets: QuixBugs and Defects4J. The results demonstrated significant improvements in the effectiveness and accuracy in comparison with existing solutions. We complement these findings through the analysis of two case studies.