Enhancing Program Repair with Multi-Objective Fine-Tuning of Large Language Models

Conceitos Básicos

MOREPAIR, a novel multi-objective fine-tuning framework, empowers open-source large language models to grasp repair logic and produce high-quality patches effectively.

Resumo

The paper proposes MOREPAIR, a multi-objective fine-tuning framework for enhancing the program repair capabilities of large language models (LLMs). The key insights are:

MOREPAIR fine-tunes LLMs with two objectives: (1) generating repaired code and (2) producing repaired code accompanied by natural language guidance that explains the repair logic. This multi-objective learning approach enables LLMs to develop a more nuanced understanding of the repair process.

MOREPAIR leverages LLM-generated guidance, which is found to be more effective than human-generated guidance in improving the reasoning capabilities of fine-tuned LLMs.

Experiments on the EvalRepair-C++ and EvalRepair-Java benchmarks show that MOREPAIR significantly outperforms standard fine-tuning approaches and state-of-the-art methods like Fine-tune-CoT and RepairLLaMA, even when the latter is provided with perfect fault location information.

MOREPAIR's effectiveness is demonstrated across LLMs of varying sizes and architectures, highlighting its generalizability. The approach is able to narrow the performance gap between small open-source models and larger closed-source models.

The introduction of the EvalRepair-C++ and EvalRepair-Java benchmarks, which include augmented test cases to mitigate patch overfitting, provides a more rigorous evaluation setup for program repair tasks.

Estatísticas

MOREPAIR achieves a 11.0% improvement in TOP-10 repair performance over the baseline CodeLlama-13B on the EvalRepair-C++ benchmark.
On the EvalRepair-Java benchmark, MOREPAIR exhibits an 8.0% increase in TOP-10 repair performance over the baseline CodeLlama-13B.
MOREPAIR outperforms the standard fine-tuning approach (STDFT) by 5.5% and 9.9% in TOP-10 repair performance on EvalRepair-C++ and EvalRepair-Java, respectively.

Citações

"MOREPAIR steers LLMs towards a precise understanding the reasoning logic behind the repair process, thereby enabling them to generate high-quality patches."
"By focusing on conversational guidance, i.e., natural language, MOREPAIR ensures that the learning is programming language-independent, making it suitable for multilingual repair scenarios."
"MOREPAIR has the ability to narrow the performance gap between small open-source models and larger closed-source models."

Principais Insights Extraídos De

Multi-Objective Fine-Tuning for Enhanced Program Repair with LLMs

by Boya... às arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12636.pdf

Perguntas Mais Profundas

How can MOREPAIR's multi-objective fine-tuning approach be extended to other software engineering tasks beyond program repair, such as code generation or code summarization?

MOREPAIR's multi-objective fine-tuning approach can be extended to other software engineering tasks by adapting the learning objectives to suit the specific requirements of tasks like code generation or code summarization. For code generation, one objective could focus on generating syntactically correct code, while another objective could emphasize generating code that adheres to specific design patterns or architectural constraints. This dual-focus approach would guide the LLM to not only produce code that compiles but also aligns with best practices in software development.
Similarly, for code summarization, the objectives could revolve around generating concise and informative summaries of code snippets. One objective could target the extraction of key functionalities or algorithms from the code, while the other could aim to provide a high-level overview of the code's purpose and functionality. By fine-tuning the LLM with these dual objectives, MOREPAIR can enhance the model's ability to generate accurate and informative code summaries.
In essence, by tailoring the learning objectives to the specific requirements of tasks like code generation or code summarization, MOREPAIR can be effectively extended to a wide range of software engineering tasks beyond program repair, enabling LLMs to excel in various aspects of code-related tasks.

What are the potential limitations of relying on LLM-generated guidance, and how can these be addressed to further improve the effectiveness of MOREPAIR?

Relying solely on LLM-generated guidance may pose certain limitations in terms of the quality and relevance of the generated guidance. Some potential limitations include:

Lack of domain-specific knowledge: LLMs may not possess domain-specific knowledge required for certain software engineering tasks, leading to inaccuracies or irrelevant guidance.
Limited context understanding: LLMs may struggle to grasp the full context of complex code repair scenarios, resulting in suboptimal guidance.
Bias in generated guidance: LLMs can inadvertently introduce biases or inaccuracies in the generated guidance, impacting the repair process.

To address these limitations and enhance the effectiveness of MOREPAIR, the following strategies can be implemented:

Incorporating domain-specific pre-training: Pre-training LLMs on domain-specific datasets can enhance their understanding of software engineering concepts, improving the relevance and accuracy of the generated guidance.
Human-in-the-loop validation: Introducing a human-in-the-loop validation process where human experts review and refine the LLM-generated guidance can help correct inaccuracies and ensure the quality of the guidance.
Ensemble learning: Combining LLM-generated guidance with human-generated guidance or guidance from multiple LLMs can mitigate biases and errors, leading to more robust and reliable guidance for the fine-tuning process.

By addressing these limitations through a combination of domain-specific pre-training, human validation, and ensemble learning techniques, MOREPAIR can overcome the challenges associated with relying solely on LLM-generated guidance, thereby improving its overall effectiveness in software engineering tasks.

Given the importance of test case coverage in program repair, how can MOREPAIR's performance be enhanced by incorporating techniques for automatically generating diverse and comprehensive test suites?

To enhance MOREPAIR's performance through the incorporation of techniques for automatically generating diverse and comprehensive test suites, the following strategies can be implemented:

Automated test case generation tools: Utilize automated test case generation tools such as EvoSuite or CodeCover to automatically generate a wide range of test cases covering various code paths and scenarios. These tools can help ensure comprehensive test coverage and identify edge cases that may not be covered by manual testing.
Mutation testing: Implement mutation testing techniques to automatically introduce small changes (mutations) to the code and assess the effectiveness of the generated patches in detecting and repairing these mutations. This approach can help validate the robustness of the generated patches and improve the overall repair quality.
Feedback-driven testing: Incorporate feedback-driven testing methodologies where the performance of the generated patches is continuously evaluated against a diverse set of test cases. Based on the feedback received, MOREPAIR can adapt its fine-tuning process to prioritize patches that demonstrate high efficacy across a wide range of test scenarios.
Adaptive test suite generation: Develop adaptive test suite generation algorithms that dynamically adjust the test suite based on the evolving codebase and repair requirements. By continuously updating and expanding the test suite, MOREPAIR can ensure that the generated patches are thoroughly evaluated under varying conditions.

By integrating these techniques for automatically generating diverse and comprehensive test suites, MOREPAIR can enhance its performance in program repair tasks by validating the effectiveness and reliability of the generated patches across a broad spectrum of test scenarios.