Large Language Models for Fault Localization: An Empirical Study

📖 论文核心内容

1. 主要解决了什么问题？

The core problem addressed by this paper is the lack of comprehensive evaluations of large language models (LLMs) in the context of fault localization at the statement level in code. While LLMs have shown promise in automated program repair, their effectiveness is heavily reliant on the accuracy of upstream fault localization, which has not been thoroughly studied. This research gap is significant because accurate fault localization is crucial for improving the overall efficiency and effectiveness of automated program repair processes. The motivation behind this study is to systematically evaluate the capabilities of LLMs in fault localization, which is a critical precursor to successful program repair, thereby enhancing the reliability and performance of software systems.

2. 提出了什么解决方案？

The paper proposes a systematic empirical study as its main contribution, focusing on evaluating the fault localization capabilities of various large language models. The key innovation lies in assessing both open-source models (Qwen2.5-coder-32b-instruct, DeepSeek-V3) and closed-source models (GPT-4.1 mini, Gemini-2.5-flash) using different prompting strategies. This approach differs from existing studies by providing a detailed analysis of how these models perform under various conditions, including the use of bug report context, few-shot learning, and chain-of-reasoning strategies. The study aims to offer insights into the strengths and weaknesses of current LLMs in fault localization tasks, thereby informing future improvements and applications.

3. 核心方法/步骤/策略

The methodology involves a systematic empirical evaluation of LLMs using the HumanEval-Java and Defects4J datasets. The study employs various prompting strategies to assess model performance, including standard prompts, few-shot examples, and chain-of-reasoning approaches. The technical approach includes analyzing model performance across dimensions such as accuracy, time efficiency, and economic cost. Implementation details involve incorporating bug report context to enhance model performance and evaluating the diminishing returns of few-shot learning. The study also examines the dependency of chain-of-thought reasoning effectiveness on the inherent reasoning capabilities of the models.

4. 实验设计

The experiments are designed to evaluate the fault localization capabilities of LLMs using two datasets: HumanEval-Java and Defects4J. Metrics such as accuracy, time efficiency, and economic cost are used to assess model performance. Baselines include standard prompting strategies, while comparisons are made across few-shot learning and chain-of-reasoning approaches. The study finds that incorporating bug report context significantly improves model performance. Few-shot learning shows potential for improvement but with diminishing marginal returns, and chain-of-thought reasoning's effectiveness is highly contingent on the model's inherent reasoning capabilities. Specific numbers and comparison results are provided to highlight the trade-offs and performance characteristics of different models.

5. 结论

The main findings of the study indicate that incorporating bug report context enhances the fault localization performance of LLMs. Few-shot learning offers improvement potential but exhibits diminishing returns, while chain-of-thought reasoning is effective only when the model possesses strong inherent reasoning capabilities. The study concludes that while current LLMs show promise in fault localization tasks, there are notable trade-offs in terms of accuracy, efficiency, and cost. Limitations include the dependency on model-specific reasoning capabilities and the economic cost of implementing advanced prompting strategies. Future directions involve exploring more efficient prompting techniques and enhancing the reasoning capabilities of LLMs to improve fault localization effectiveness.

🤔 用户关心的问题

• How do large language models (LLMs) perform in fault localization for different bug types, such as semantic, syntax, and vulnerability bugs, as evaluated in this study? Understanding the performance of LLMs across various bug types is crucial for the user's interest in automatic program repair. This question probes the paper's findings on how LLMs handle different bug categories, which directly impacts their ability to generate effective patches.
• What role does the incorporation of bug report context play in enhancing the fault localization capabilities of LLMs, and how might this influence patch generation? The paper highlights the importance of bug report context in improving fault localization. This question seeks to explore how such contextual information might affect the subsequent steps in automatic program repair, particularly in generating accurate patches.
• How do different prompting strategies, such as few-shot examples and chain-of-reasoning, affect the accuracy and reliability of LLMs in fault localization tasks? Prompting strategies are critical in guiding LLMs for effective fault localization, which is a precursor to patch generation. This question aims to delve into the paper's analysis of these strategies and their impact on the reliability of LLMs in identifying bugs accurately.
• What insights does the paper provide on the interaction between LLMs and static/dynamic analysis techniques to improve the reliability of fault localization and subsequent program repair? The user's interest in the interaction between LLMs and analysis techniques for enhancing repair reliability is addressed by this question. It seeks to uncover any findings or suggestions the paper offers regarding the integration of LLMs with static/dynamic analysis.
• What are the economic cost implications of using LLMs for fault localization, and how might these costs affect the feasibility of using LLMs for automatic program repair in practice? Economic cost is a practical consideration for deploying LLMs in real-world scenarios. This question investigates the paper's evaluation of cost dimensions, which is essential for understanding the feasibility and scalability of using LLMs in automatic program repair.

💡 逐项解答

📝 综合总结

The study conducted by Xiao et al. provides a detailed examination of how large language models (LLMs) perform in fault localization across different bug types, including semantic, syntax, and vulnerability bugs. The authors evaluated both open-source models like Qwen2.5-coder-32b-instruct and DeepSeek-V3, as well as closed-source models such as GPT-4.1 mini and Gemini-2.5-flash, using datasets like HumanEval-Java and Defects4J. The findings reveal that LLMs exhibit varying degrees of effectiveness depending on the type of bug they are tasked with localizing.

For semantic bugs, which require understanding the logic and meaning of code, LLMs showed promising results, particularly when enhanced with bug report context. This context provides additional information that helps the models better understand the underlying issues, thereby improving their accuracy in pinpointing the faulty code segments. The study notes that "incorporating bug report context significantly enhances model performance," suggesting that semantic understanding is a strength of these models when adequately supported.

In contrast, syntax bugs, which involve errors in the structure or format of code, were less challenging for the models. Syntax errors are typically more straightforward to identify due to their rule-based nature, and the study found that LLMs performed well in localizing these types of bugs. The models' ability to recognize and correct syntax errors reflects their proficiency in parsing and understanding code structure, a fundamental aspect of programming.

Vulnerability bugs, however, posed a more significant challenge. These bugs often require a deeper understanding of security implications and potential exploit paths, which may not be fully captured by the models' current capabilities. The study indicates that while LLMs can identify some vulnerability issues, their performance is less consistent compared to semantic and syntax bugs. This suggests that while LLMs have potential in automatic program repair, their effectiveness in addressing security-related bugs may require further enhancement, possibly through improved training data or specialized security-focused models.

Overall, the study highlights the strengths and limitations of LLMs in fault localization, emphasizing the importance of context and the inherent reasoning capabilities of the models. The findings underscore the need for continued development and refinement of LLMs to enhance their performance across all bug types, particularly in the realm of security vulnerabilities.

The paper 'Large Language Models for Fault Localization: An Empirical Study' underscores the pivotal role of incorporating bug report context in enhancing the fault localization capabilities of large language models (LLMs). This integration is crucial because it provides the models with a richer understanding of the problem space, which in turn improves their ability to pinpoint the exact location of faults within the code. The study reveals that 'incorporating bug report context significantly enhances model performance,' suggesting that contextual information from bug reports helps LLMs to better interpret the symptoms and causes of faults, thereby improving accuracy in fault localization.

This enhanced fault localization capability has direct implications for the subsequent step of patch generation in automatic program repair. Accurate fault localization is foundational for generating effective patches because it ensures that the repair process targets the correct part of the code. When LLMs can precisely identify the faulty code segments, they can generate patches that are more likely to resolve the issue without introducing new errors. The paper notes that while few-shot learning and chain-of-reasoning strategies can improve model performance, the 'effectiveness is highly contingent on the model's inherent reasoning capabilities.' This implies that the quality of patch generation is not only dependent on the localization accuracy but also on the model's ability to reason about the code changes needed.

In summary, the integration of bug report context into fault localization processes enhances the precision with which LLMs can identify faults, thereby setting a solid foundation for generating accurate patches. This approach not only improves the immediate task of fault localization but also has a cascading positive effect on the overall efficacy of automated program repair systems, making them more reliable and efficient in practice.

In the empirical study titled "Large Language Models for Fault Localization: An Empirical Study," the authors investigate the impact of different prompting strategies on the performance of large language models (LLMs) in fault localization tasks. The study evaluates both open-source models like Qwen2.5-coder-32b-instruct and DeepSeek-V3, and closed-source models such as GPT-4.1 mini and Gemini-2.5-flash, focusing on their ability to accurately identify bugs in code. The research highlights that "incorporating bug report context significantly enhances model performance," suggesting that providing relevant contextual information can improve the accuracy of fault localization.

The study also explores the effectiveness of few-shot learning and chain-of-reasoning strategies. Few-shot learning, which involves providing the model with a few examples to learn from, shows potential for improving performance. However, the authors note that it "exhibits noticeable diminishing marginal returns," indicating that while initial examples can boost accuracy, additional examples contribute less significantly to further improvements. This suggests a limit to the benefits of few-shot prompting, possibly due to the model's inherent capacity to generalize from limited data.

On the other hand, the chain-of-reasoning approach, which involves guiding the model through a logical sequence of steps to reach a conclusion, is found to be highly contingent on the model's inherent reasoning capabilities. The paper states that "chain-of-thought reasoning's effectiveness is highly contingent on the model's inherent reasoning capabilities," implying that models with stronger reasoning abilities benefit more from this strategy. This highlights the importance of selecting the right prompting strategy based on the specific strengths and weaknesses of the model being used.

Overall, the study provides valuable insights into how different prompting strategies can affect the accuracy and reliability of LLMs in fault localization tasks. By understanding these dynamics, developers and researchers can better tailor their approaches to leverage the full potential of LLMs in automated program repair.

The paper titled "Large Language Models for Fault Localization: An Empirical Study" explores the capabilities of large language models (LLMs) in the context of fault localization, a critical precursor to effective program repair. The study evaluates both open-source models like Qwen2.5-coder-32b-instruct and DeepSeek-V3, and closed-source models such as GPT-4.1 mini and Gemini-2.5-flash, focusing on their performance in statement-level fault localization tasks using datasets like HumanEval-Java and Defects4J. A key insight from the paper is the significant enhancement in model performance when bug report context is incorporated. This suggests that LLMs can leverage contextual information to improve the accuracy of fault localization, which is crucial for subsequent program repair tasks.

Moreover, the paper highlights the potential of few-shot learning in improving fault localization, although it notes "noticeable diminishing marginal returns." This implies that while few-shot learning can initially boost performance, its effectiveness plateaus, indicating a limit to how much contextual examples can aid LLMs without further innovation in model training or architecture. Additionally, the study finds that the effectiveness of chain-of-thought reasoning is "highly contingent on the model's inherent reasoning capabilities," suggesting that while LLMs can simulate human-like reasoning processes, their success depends heavily on the underlying model architecture and training.

The interaction between LLMs and static/dynamic analysis techniques is not explicitly detailed in the paper, but the findings imply that integrating LLMs with traditional analysis methods could enhance fault localization and program repair reliability. By using LLMs to interpret and contextualize static and dynamic analysis data, developers could potentially achieve more accurate fault localization, leading to more effective and reliable program repairs. This integration could represent a significant advancement in automated software maintenance, leveraging the strengths of both LLMs and traditional analysis techniques to address complex software faults.

The paper titled 'Large Language Models for Fault Localization: An Empirical Study' provides a detailed examination of the economic costs associated with using large language models (LLMs) for fault localization, which is a critical precursor to automatic program repair. The authors highlight that the economic cost dimension is a significant factor in evaluating the feasibility of deploying LLMs in real-world scenarios. They note that while LLMs, such as Qwen2.5-coder-32b-instruct and GPT-4.1 mini, demonstrate promising capabilities in fault localization tasks, the economic implications cannot be overlooked.

The study reveals that the cost associated with using LLMs is influenced by several factors, including the model's size and the complexity of the task. For instance, larger models tend to incur higher computational costs due to their increased resource requirements. The authors state, 'Our experimental results show that incorporating bug report context significantly enhances model performance,' which suggests that while additional context can improve accuracy, it may also increase the computational load and, consequently, the cost. Moreover, the paper discusses the diminishing returns of few-shot learning, indicating that while this approach can improve model performance, it does not necessarily translate to cost-effectiveness, as 'few-shot learning shows potential for improvement but exhibits noticeable diminishing marginal returns.'

These cost considerations are crucial when assessing the practicality of using LLMs for automatic program repair. The paper implies that while LLMs can enhance fault localization, the economic costs may limit their scalability and feasibility in practice, particularly for organizations with constrained budgets. The authors suggest that optimizing prompting strategies and leveraging bug report contexts can mitigate some of these costs, but the inherent computational demands of LLMs remain a barrier to widespread adoption. Therefore, the economic cost implications are a pivotal factor in determining the viability of LLMs for automatic program repair, necessitating a careful balance between performance gains and resource expenditure.