Enhancing Conversation-Based Automated Program Repair with ContrastRepair
ContrastRepair improves program repair by providing informative feedback to Large Language Models, outperforming existing methods.
Efficient Program Repair with RepairLLaMA: Fine-Tuned Adapters and Code Representations
RepairLLaMA combines tailored code representations and parameter-efficient fine-tuning to enhance program repair effectiveness, outperforming baselines in fixing bugs with language models.
Empowering Automated Program Repair Across Languages Through Checkpoint Ensemble
T5APR introduces a novel multilingual neural repair approach, leveraging transformer models, to efficiently fix bugs across various programming languages.
ContrastRepair: Enhancing Automated Program Repair with Contrastive Test Cases
ContrastRepair significantly improves program repair efficiency by providing informative prompts to Large Language Models through contrastive test cases.
T5APR: Empowering Automated Program Repair Across Languages Through Checkpoint Ensemble
T5APR is a novel neural program repair approach that offers a unified solution for bug fixing across multiple programming languages, showcasing competitive performance against state-of-the-art techniques.
ContrastRepair: Enhancing Automated Program Repair with Contrastive Test Cases
ContrastRepair significantly improves program repair efficiency by providing informative feedback to Large Language Models through contrastive test cases.
Multi-Task Program Error Repair and Explanatory Diagnosis using Machine Learning
This paper proposes mPRED, a novel machine-learning approach for multi-task program error repair and explanatory diagnosis, aiming to improve the accuracy and efficiency of identifying and fixing program errors while providing clear explanations to programmers.
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