Program Repair
34 papers with code • 3 benchmarks • 8 datasets
Task of teaching ML models to modify an existing program to fix a bug in a given code.
Benchmarks
These leaderboards are used to track progress in Program Repair
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Datasets
DeepFix
xCodeEval
TFix's Code Patches Data
Subtasks
Latest papers with no code
Enhancing Genetic Improvement Mutations Using Large Language Models
We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits.
Automated Bug Generation in the era of Large Language Models
From the classic software engineering point of view, a hard-to-repair bug differs from the correct code in multiple locations, making it hard to localize and repair.
Program Repair with Minimal Edits Using CodeT5
The experimental results show that the fine-tuned CodeT5 achieves a pass@100 of 91. 95% and an average edit distance of the most similar correct program of 6. 84, which indicates that at least one correct program can be suggested by generating 100 candidate programs.
Frustrated with Code Quality Issues? LLMs can Help!
We present a tool, CORE (short for COde REvisions), architected using a pair of LLMs organized as a duo comprised of a proposer and a ranker.
RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair
Automatic program repair (APR) is crucial to reduce manual debugging efforts for developers and improve software reliability.
An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code
Large language models trained on source code can support a variety of software development tasks, such as code recommendation and program repair.
Better patching using LLM prompting, via Self-Consistency
Large Language models (LLMs) can be induced to solve non-trivial problems with "few-shot" prompts including illustrative problem-solution examples.
Is ChatGPT the Ultimate Programming Assistant -- How far is it?
To assess the feasibility of using an LLM as a useful assistant bot for programmers, we must assess its realistic capabilities on unseen problems as well as its capabilities on various tasks.
Fully Autonomous Programming with Large Language Models
Current approaches to program synthesis with Large Language Models (LLMs) exhibit a "near miss syndrome": they tend to generate programs that semantically resemble the correct answer (as measured by text similarity metrics or human evaluation), but achieve a low or even zero accuracy as measured by unit tests due to small imperfections, such as the wrong input or output format.
Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering
We applied PLBART and CodeT5, two state-of-the-art language models that are pre-trained with both PL and NL, on two such natural language-based program repair datasets and found that the pre-trained language models fine-tuned with datasets containing both code review and subsequent code changes notably outperformed each of the previous models.
