Evaluating Large Language Models for Repository-Level Program Repair

Основные понятия

The authors investigate the performance of popular LLMs in handling repository-level repair tasks and propose a new benchmark, RepoBugs, along with a context extraction method (RLCE) to enhance repair accuracy significantly.

Аннотация

In recent years, large language models (LLMs) have shown potential in automatic program repair (APR) tasks. The study focuses on repository-level context for bug fixes. RepoBugs benchmark reveals the importance of precise context for LLMs. RLCE method enhances LLMs' ability to handle repository-level bugs by 160%. Different prompt strategies impact model performance. Explanation accuracy is crucial for CoT strategy.

The study compares different models and methods using a variety of evaluation metrics. RLCE outperforms baselines like preliminary and slice-similarity methods. Context sources like definitions of EIF play a vital role in repair accuracy. Error types like ORV and CRV pose challenges for LLMs. Prompt length affects repair accuracy, with longer prompts not always leading to better results.

Статистика

Preliminary method only use the function where the error was located as context.
The repair rate on RepoBugs is only 22.58% using GPT3.5 based on function-level context.
RLCE enhances the ability to repair repository-level bugs by up to 160%.

Цитаты

"Providing repository-level context is helpful when dealing with repository-level bugs in LLMs."
"RLCE significantly enhances the performance of LLMs in addressing repository-level APR tasks."

Ключевые выводы из

When Large Language Models Confront Repository-Level Automatic Program Repair

by Yuxiao Chen,... в arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00448.pdf

Дополнительные вопросы

How can the findings from this study be applied practically in software development?

The findings from this study provide valuable insights into the performance of large language models (LLMs) in handling repository-level automatic program repair tasks. Practically, these findings can be applied to enhance automated program repair processes by emphasizing the importance of providing precise repository-level context for LLMs. By utilizing methods like RLCE to extract relevant context and generate effective prompts for LLMs, developers can improve the accuracy and efficiency of bug fixes at a broader level within code repositories. This approach could streamline the debugging process, reduce manual intervention, and ultimately enhance software quality and reliability.

What are some potential drawbacks or limitations of relying heavily on large language models for program repairs?

While large language models offer significant potential for automating program repairs, there are several drawbacks and limitations to consider when relying heavily on them:

Limited Understanding: LLMs may lack deep semantic understanding of code logic compared to human programmers, leading to inaccuracies in complex repair scenarios.
Overfitting: There is a risk that LLMs may memorize common patterns rather than truly understanding underlying principles, potentially resulting in suboptimal fixes.
Bias: Models trained on biased data may perpetuate biases in their repair suggestions.
Scalability: Processing large codebases with LLMs can be computationally intensive and time-consuming.
Interpretability: Understanding how an LLM arrived at a specific fix can be challenging due to their black-box nature.

How might advancements in AI technology impact traditional software engineering practices?

Advancements in AI technology have the potential to significantly impact traditional software engineering practices:

Automated Code Generation: AI tools like GPT-3 can assist developers by generating code snippets based on natural language descriptions or requirements.
Bug Detection and Repair: AI-powered tools can help identify bugs through static analysis or dynamic testing techniques and even propose automated fixes using machine learning algorithms.
Enhanced Testing Automation: AI-driven testing frameworks can optimize test case generation, execution, and result analysis, improving overall test coverage and effectiveness.
Code Reviews : Natural Language Processing (NLP) capabilities integrated into AI systems enable more efficient code reviews by identifying inconsistencies or vulnerabilities based on coding standards automatically.

These advancements will likely lead to increased productivity, faster development cycles, improved code quality assurance processes while also necessitating adaptations in skill sets required for future software engineers who will need proficiency not only in programming but also working alongside intelligent machines effectively.