Leveraging Large Language Models for Automated Program Repair without Fault Localization

Core Concepts

Aligning the output format of large language models (LLMs) to their pre-training objective and allowing them to locate and repair bugs simultaneously can significantly improve their automated program repair (APR) performance without relying on fault localization tools.

Abstract

The paper investigates a new approach to adapt LLMs for automated program repair (APR). The key insights are: Modifying the output format of LLMs from discrete fixed hunks to the entire refined program can better align the inference objective with the training objective of decoder-only LLMs, leading to significant performance improvement in APR. Prompting LLMs with buggy programs and corresponding artifacts (e.g., failed tests, error messages) can enable them to locate and repair bugs simultaneously without relying on fault localization tools, further enhancing their APR performance. Based on these insights, the authors developed D4C, a straightforward prompting framework for APR. D4C can repair 180 bugs correctly in Defects4J, outperforming state-of-the-art APR methods with perfect fault localization by 10% and reducing the patch sampling number by 90%. The findings reveal that objective alignment and replacing the traditional localize-then-repair workflow with direct debugging are crucial for harnessing LLMs in APR.

Stats

The paper reports the following key statistics: D4C can repair 180 out of 437 single-function bugs in Defects4J, outperforming state-of-the-art APR methods with perfect fault localization by 10%. D4C only needs to sample 10 patches per bug, which is 90% fewer than the most efficient baseline (100-5000 samples).

Quotes

"Aligning the output from infilling discrete hunks to completing entire functions can better attain the training objective." "Allowing LLM to locate and repair bugs with artifacts in a human-like manner can further improve its APR performance."

Key Insights Distilled From

Aligning LLMs for FL-free Program Repair

by Junjielong X... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08877.pdf

Deeper Inquiries

How can the insights from D4C be extended to other LLM-based software engineering tasks beyond program repair?

The insights from D4C can be extended to various LLM-based software engineering tasks beyond program repair by focusing on aligning the model's training objective with the task at hand and providing relevant artifacts to enhance the model's understanding and performance. For tasks like code summarization, code completion, code generation, and code recommendation, ensuring that the output format aligns with the model's training objective can significantly improve the results. By prompting the model with specific instructions and examples, similar to how D4C constructs bug reports, LLMs can be guided to generate more accurate and contextually relevant outputs for a wide range of software engineering tasks. Additionally, incorporating artifacts such as code documentation, test cases, and error messages can help LLMs better understand the context and requirements of the task, leading to more effective and precise outcomes.

What are the potential limitations of the direct debugging approach, and how can they be addressed in future work?

One potential limitation of the direct debugging approach used in D4C is the reliance on the quality and availability of artifacts such as documentation, test cases, and error messages. If these artifacts are incomplete, inaccurate, or missing, it can impact the model's ability to accurately locate and repair bugs. To address this limitation, future work could focus on developing techniques to enhance artifact extraction and processing, including methods for automatically generating or augmenting missing artifacts. Additionally, incorporating techniques for handling noisy or incomplete artifacts and providing the model with mechanisms to adapt to varying levels of artifact quality could improve the robustness and effectiveness of the direct debugging approach. Another limitation could be the scalability of the approach to handle larger and more complex codebases. As software projects grow in size and complexity, the direct debugging approach may face challenges in efficiently processing and analyzing extensive code repositories. Future work could explore strategies for optimizing the artifact processing and prompting techniques to handle larger codebases effectively. This could involve leveraging parallel processing, distributed computing, or advanced data processing techniques to enhance the scalability of the approach.

How can the prompting and artifact processing techniques used in D4C be generalized to handle more complex program structures beyond single functions?

To generalize the prompting and artifact processing techniques used in D4C to handle more complex program structures beyond single functions, several approaches can be considered. Hierarchical Prompting: Instead of focusing on individual functions, prompts can be designed to guide the model in understanding and repairing larger code structures, such as classes, modules, or entire applications. By providing hierarchical prompts that capture the relationships and dependencies between different parts of the codebase, LLMs can be trained to address complex program structures effectively. Contextual Artifact Processing: Enhancing artifact processing techniques to capture and utilize contextual information from multiple sources within a codebase can help LLMs better understand and navigate complex program structures. By integrating information from code comments, version control history, and external libraries, the model can gain a more comprehensive understanding of the codebase and make more informed repair decisions. Semantic Analysis: Incorporating semantic analysis techniques into artifact processing can enable LLMs to extract and utilize higher-level information about the code, such as variable dependencies, control flow patterns, and data structures. By analyzing the semantics of the codebase, LLMs can generate more contextually relevant and accurate repairs for complex program structures. By incorporating these strategies, the prompting and artifact processing techniques used in D4C can be adapted and extended to handle more intricate and multifaceted program structures in software engineering tasks.