Related papers: MCRepair: Multi-Chunk Program Repair via Patch Opt…
Large Language Models (LLMs) have recently shown strong potential in automatic program repair (APR), especially in repository-level settings where the goal is to generate patches based on natural language issue descriptions, large…
Fault localization, aiming at localizing the root cause of the bug under repair, has been a longstanding research topic. Although many approaches have been proposed in the last decades, most of the existing studies work at coarse-grained…
This paper introduces DSrepair, a knowledge-enhanced program repair method designed to repair the buggy code generated by LLMs in the data science domain. DSrepair uses knowledge graph based RAG for API knowledge retrieval as well as bug…
Fixing bugs in large programs is a challenging task that demands substantial time and effort. Once a bug is found, it is reported to the project maintainers, who work with the reporter to fix it and eventually close the issue. However,…
Despite the immense popularity of the Automated Program Repair (APR) field, the question of patch validation is still open. Most of the present-day approaches follow the so-called Generate-and-Validate approach, where first a candidate…
Automated program repair has emerged as a powerful technique to mitigate the impact of software bugs on system reliability and user experience. This paper introduces RepairAgent, the first work to address the program repair challenge…
Automated test generators, such as search based software testing (SBST) techniques, replace the tedious and expensive task of manually writing test cases. SBST techniques are effective at generating tests with high code coverage. However,…
In supporting the development of high-quality software, especially necessary in the era of LLMs, automated program repair (APR) tools aim to improve code quality by automatically addressing violations detected by static analysis profilers.…
Automated Program Repair (APR) techniques aim to automatically fix buggy programs. Among these, Large Language Model-based (LLM-based) approaches have shown great promise. Recent advances demonstrate that directly leveraging LLMs can…
Automated Program Repair (APR) aims to fix bugs by generating patches. And existing work has demonstrated that "pre-training and fine-tuning" paradigm enables Large Language Models (LLMs) improve fixing capabilities on APR. However,…
Neural network-based techniques for automated program repair are becoming increasingly effective. Despite their success, little is known about why they succeed or fail, and how their way of reasoning about the code to repair compares to…
Automated Program Repair (APR) has advanced rapidly with Large Language Models (LLMs), but most existing methods remain computationally expensive, and focused on a small set of languages. Ruby, despite its widespread use in web development…
In the context of test case based automated program repair (APR), the research community call the patches that pass all the test cases but fail to actually fix the bug test case overfitted patches. Currently, overfitted patches has to be…
Benchmarks play an important role in evaluating the efficiency and effectiveness of solutions to automate several phases of the software development lifecycle. Moreover, if well designed, they also serve us well as an important artifact to…
Research shows that errors in natural language can be corrected by translating texts to another language and back using language models. We explore to what extent this latent correction capability extends to Automated Program Repair (APR)…
We study the problem of centralized exact repair of multiple failures in distributed storage. We describe constructions that achieve a new set of interior points under exact repair. The constructions build upon the layered code construction…
Context: Given a bug report and source code of the project, bug localization can help developers to focus on fixing probable buggy files rather than searching the entire source code repository. While existing research uses information…
Ensuring code correctness remains a challenging problem even as large language models (LLMs) become increasingly capable at code-related tasks. While LLM-based program repair systems can propose bug fixes using only a user's bug report,…
IR-based fault localization approaches achieves promising results when locating faulty files by comparing a bug report with source code. Unfortunately, they become less effective to locate faulty methods. We conduct a preliminary study to…
Context: Bug-fix pattern detection has been investigated in the past in the context of classical software. However, while quantum software is developing rapidly, the literature still lacks automated methods and tools to identify, analyze,…