A Comprehensive Evaluation of ChatGPT's Automated Program Repair Capabilities on a Novel Benchmark

Core Concepts

ChatGPT, a prominent large language model, demonstrates impressive capabilities in automatically repairing buggy programs from a novel benchmark dataset, EvalGPTFix, outperforming state-of-the-art models.

Abstract

The paper presents a comprehensive evaluation of the automated program repair (APR) capabilities of ChatGPT, a large language model (LLM), on a newly constructed benchmark dataset called EvalGPTFix. The key highlights are:

Construction of EvalGPTFix: The authors create a new APR benchmark dataset containing 151 pairs of buggy and fixed Java programs from recent competitive programming contests on AtCoder. This ensures the dataset is not seen by ChatGPT during its training.

Effectiveness of ChatGPT: When provided with only the buggy code, ChatGPT is able to fix 109 out of the 151 bugs, outperforming state-of-the-art LLMs CodeT5 and PLBART by 27.5% and 62.4% in prediction accuracy, respectively.

Impact of Prompts: The authors investigate the effect of providing additional information in the prompts, such as problem descriptions, error feedback, and bug localization. This leads to fixing an additional 34 bugs that were not fixed with the basic prompt.

Dialogue-based Repair: The authors further explore the impact of a dialogue-based repair workflow, where ChatGPT is provided feedback on its previous patches. This allows ChatGPT to fix 9 more bugs that were not fixed with the previous prompts.

Challenges and Opportunities: The paper discusses the challenges of data leakage when evaluating black-box LLMs on code-related tasks, and highlights opportunities for advanced software engineering studies equipped with such LLMs.

Overall, the study demonstrates the impressive program repair capabilities of ChatGPT, while also revealing the importance of carefully designing benchmarks and prompts to accurately assess the performance of LLMs in software engineering tasks.

Stats

ChatGPT fixes 109 out of 151 bugs using the basic prompt.
ChatGPT fixes 143 out of 151 bugs in total, including the additional fixes from advanced prompts and dialogues.
ChatGPT outperforms CodeT5 and PLBART by 27.5% and 62.4% in prediction accuracy, respectively.

Quotes

"ChatGPT is able to fix 109 out of 151 bugs using the basic prompt within 35 independent rounds, outperforming state-of-the-art LLMs CodeT5 and PLBART by 27.5% and 62.4% prediction accuracy."
"Besides, we provide additional discussion from the interactive nature of ChatGPT to illustrate the capacity of a dialog-based repair workflow with 9 additional fixed bugs."

Key Insights Distilled From

A Critical Review of Large Language Model on Software Engineering: An Example from ChatGPT and Automated Program Repair

by Quanjun Zhan... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2310.08879.pdf

Deeper Inquiries

How can the findings from this study be extended to other software engineering tasks beyond automated program repair?

The findings from this study can be extended to other software engineering tasks by leveraging the capabilities of large language models (LLMs) like ChatGPT in various domains. For instance, the success of ChatGPT in fixing bugs in the context of automated program repair demonstrates its potential in tasks such as code summarization, code completion, and even natural language processing for requirements engineering. By providing detailed prompts and engaging in dialogues with the model, researchers can explore the effectiveness of LLMs in a wide range of software engineering tasks. Additionally, the study highlights the importance of data leakage concerns when evaluating LLMs, which can be applied to ensure the integrity and reliability of models in different SE tasks.

What are the potential risks and ethical concerns associated with the widespread adoption of large language models like ChatGPT in software development workflows?

The widespread adoption of large language models like ChatGPT in software development workflows poses several potential risks and ethical concerns. One major risk is the issue of data leakage, as highlighted in the study, where the model may have access to training data that overlaps with evaluation datasets, leading to biased results. This can raise concerns about the model's ability to generalize to unseen data and the potential for unintended consequences in software development tasks.
Ethical concerns include the lack of transparency in the training process of LLMs, as they are often trained on proprietary datasets with unknown biases and ethical considerations. This opacity can lead to issues of accountability, fairness, and privacy when using these models in real-world applications. Moreover, there is a risk of perpetuating biases present in the training data, which can result in discriminatory outcomes in software engineering tasks.

How can the prompt engineering techniques explored in this study be generalized to improve the performance of large language models across a broader range of software engineering applications?

The prompt engineering techniques explored in this study can be generalized to improve the performance of large language models across a broader range of software engineering applications by focusing on tailored prompts that provide specific task instructions, context, and examples. By crafting prompts that guide the model towards the desired outcome, researchers can enhance the model's understanding of the task at hand and improve its performance in various SE applications.
Furthermore, incorporating detailed prompts with additional information, such as problem descriptions, error feedback, and bug localization, can help LLMs like ChatGPT better comprehend the nuances of different software engineering tasks. This approach can lead to more accurate and contextually relevant responses from the model, ultimately enhancing its effectiveness in tasks like code generation, code summarization, and software documentation. Additionally, engaging in dialogues with the model, as demonstrated in the study, can further refine the model's responses and improve its ability to address complex software engineering challenges.