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
NExT: Teaching Large Language Models to Reason about Code Execution
A fundamental skill among human developers is the ability to understand and reason about program execution.
Aligning LLMs for FL-free Program Repair
Our core insight is that LLM's APR capability can be greatly improved by simply aligning the output to their training objective and allowing them to refine the whole program without first performing fault localization.
Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments
Automated generation of feedback on programming assignments holds significant benefits for programming education, especially when it comes to advanced assignments.
RepairAgent: An Autonomous, LLM-Based Agent for Program Repair
Unlike existing deep learning-based approaches, which prompt a model with a fixed prompt or in a fixed feedback loop, our work treats the LLM as an agent capable of autonomously planning and executing actions to fix bugs by invoking suitable tools.
A Study of Vulnerability Repair in JavaScript Programs with Large Language Models
In recent years, JavaScript has become the most widely used programming language, especially in web development.
DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models
We show that the task is difficult as it requires the model to learn long-range code relationships, a task that inherently relies on extensive amounts of training data.
A Novel Approach for Automatic Program Repair using Round-Trip Translation with Large Language Models
We investigate whether this correction capability of Large Language Models (LLMs) extends to Automatic Program Repair (APR).
Nova$^+$: Generative Language Models for Binaries
We build Nova$^+$ to further boost Nova using two new pre-training tasks, i. e., optimization generation and optimization level prediction, which are designed to learn binary optimization and align equivalent binaries.
ConDefects: A New Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair
With the growing interest on Large Language Models (LLMs) for fault localization and program repair, ensuring the integrity and generalizability of the LLM-based methods becomes paramount.
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.
