Automatic Patch Correctness Assessment with Large Language Model

Concetti Chiave

Automated Program Repair techniques face an overfitting problem, addressed by LLM4PatchCorrect using a large language model for code to assess patch correctness without manual labeling.

Sintesi

The content discusses the challenges of Automated Program Repair (APR) tools facing an overfitting problem despite passing all tests. It introduces LLM4PatchCorrect, a solution that leverages a large language model for code to automatically assess patch correctness without manual labeling. The article outlines the process of obtaining similar patches from existing APR tools, incorporating diverse guiding information, and conducting LLM inference on patch correctness. Experimental settings, dataset details, cross-tool validation, and baseline methods are also discussed.
Structure:

Introduction to APR tools and the overfitting problem.
Proposal of LLM4PatchCorrect for automatic patch correctness assessment.
Obtaining similar patches from existing APR tools.
Incorporating diverse guiding information for accurate predictions.
Conducting LLM inference on patch correctness.
Experimental setting with dataset details and cross-tool validation.
Baseline methods including Patch-Sim, CodeBERT, and Tian et al.'s approach.

Statistiche

Our experimental results showed that LLM4PatchCorrect can achieve an accuracy of 84.4% and an F1-score of 86.5% on average although no labeled patch of the new or unseen APR tool is available.

Citazioni

"Identifying overfitting patches is crucial for the APR tool adoption in practice."
"LLM4PatchCorrect leverages bug descriptions, execution traces, failing test cases, test coverage, and labeled patches generated by existing APR tools."
"Our proposed technique outperformed the prior state-of-the-art by a large margin."

Approfondimenti chiave tratti da

PatchZero

by Xin Zhou,Bow... alle arxiv.org 03-26-2024

https://arxiv.org/pdf/2303.00202.pdf

Domande più approfondite

How can automated program repair tools be improved to reduce false positives?

Automated program repair tools can be enhanced to reduce false positives by incorporating advanced techniques for patch correctness assessment. One approach is to leverage large language models (LLMs) for code, such as Starcoder-7B, in the assessment process. By utilizing LLMs, the tools can benefit from their pre-trained knowledge of code and context understanding to make more accurate predictions about the correctness of generated patches. Additionally, integrating diverse guiding information like bug descriptions, execution traces, failing test cases, and test coverage metrics can provide a comprehensive view of the patch's effectiveness beyond just passing tests. This holistic approach helps in identifying overfitting patches that may pass all available tests but still fail to address the underlying issues in the code.

What are the potential limitations of relying solely on large language models for code in assessing patch correctness?

While large language models (LLMs) offer significant advantages in assessing patch correctness due to their ability to understand complex code structures and contexts, there are some limitations associated with relying solely on them for this task. One key limitation is related to fine-tuning requirements - training an LLM specifically for each new APR tool or scenario might demand extensive computational resources and labeled data which may not always be readily available. Moreover, LLMs might struggle with domain-specific nuances or edge cases that require specialized knowledge beyond what they have been pre-trained on. Additionally, interpreting results from LLMs without proper human validation could lead to misinterpretations or biases in assessing patch correctness.

How might advancements in natural language processing impact automated program repair techniques in the future?

Advancements in natural language processing (NLP) are poised to have a profound impact on automated program repair techniques moving forward. NLP innovations like transformer-based models have already shown promise in understanding and generating code snippets effectively through pre-training tasks like predicting next tokens based on context. In the future, we can expect further improvements where NLP models will better comprehend programming languages' intricacies and semantics leading towards more accurate automatic bug fixing solutions.
Furthermore,
the integration of NLP capabilities into automated program repair tools could streamline processes such as bug identification through natural language bug reports analysis
and improve communication between developers and these tools making it easier for programmers at all levels of expertise
to utilize them effectively.
Overall,
advancements
in NLP hold great potential
for enhancing automation,
efficiency,
and accuracy within software development workflows particularly
in areas involving debugging
and error correction.