Demystifying Faulty Code with LLM: Step-by-Step Reasoning for Explainable Fault Localization

• URL: http://arxiv.org/abs/2403.10507v1
• Date: Fri, 15 Mar 2024 17:47:20 GMT
• Title: Demystifying Faulty Code with LLM: Step-by-Step Reasoning for Explainable Fault Localization
• Authors: Ratnadira Widyasari, Jia Wei Ang, Truong Giang Nguyen, Neil Sharma, David Lo,
• Abstract summary: This study investigates the step-by-step reasoning for explainable fault localization. We created a dataset of faulty code files, along with explanations for 600 faulty lines. We found that for 22 out of the 30 randomly sampled cases, FuseFL generated correct explanations.
• Score: 5.7821087202452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fault localization is a critical process that involves identifying specific program elements responsible for program failures. Manually pinpointing these elements, such as classes, methods, or statements, which are associated with a fault is laborious and time-consuming. To overcome this challenge, various fault localization tools have been developed. These tools typically generate a ranked list of suspicious program elements. However, this information alone is insufficient. A prior study emphasized that automated fault localization should offer a rationale. In this study, we investigate the step-by-step reasoning for explainable fault localization. We explore the potential of Large Language Models (LLM) in assisting developers in reasoning about code. We proposed FuseFL that utilizes several combinations of information to enhance the LLM results which are spectrum-based fault localization results, test case execution outcomes, and code description (i.e., explanation of what the given code is intended to do). We conducted our investigation using faulty code from Refactory dataset. First, we evaluate the performance of the automated fault localization. Our results demonstrate a more than 30% increase in the number of successfully localized faults at Top-1 compared to the baseline. To evaluate the explanations generated by FuseFL, we create a dataset of human explanations that provide step-by-step reasoning as to why specific lines of code are considered faulty. This dataset consists of 324 faulty code files, along with explanations for 600 faulty lines. Furthermore, we also conducted human studies to evaluate the explanations. We found that for 22 out of the 30 randomly sampled cases, FuseFL generated correct explanations.

Related papers

• A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why? [9.246899995643918]
  Large Language Models can still generate defective code that deviates from the specification. Seven categories of non-syntactic mistakes were identified through extensive manual analyses. Our evaluation demonstrated that GPT-4 with the ReAct prompting technique can achieve an F1 score of up to 0.65 when identifying reasons for LLM's mistakes.
  arXiv  Detail & Related papers  (2024-11-03T02:47:03Z)
• Language Agents Meet Causality -- Bridging LLMs and Causal World Models [50.79984529172807]
  We propose a framework that integrates causal representation learning with large language models. This framework learns a causal world model, with causal variables linked to natural language expressions. We evaluate the framework on causal inference and planning tasks across temporal scales and environmental complexities.
  arXiv  Detail & Related papers  (2024-10-25T18:36:37Z)
• Understanding Defects in Generated Codes by Language Models [0.669087470775851]
  This study categorizes and analyzes 367 identified defects from code snippets generated by Large Language Models. Error categories indicate key areas where LLMs frequently fail, underscoring the need for targeted improvements. This paper implemented five prompt engineering techniques, including Scratchpad Prompting, Program of Thoughts Prompting, Chain-of-Thought Prompting, Chain-of-Thought Prompting, and Structured Chain-of-Thought Prompting.
  arXiv  Detail & Related papers  (2024-08-23T21:10:09Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
  Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions. We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs [65.2379940117181]
  We introduce code prompting, a chain of prompts that transforms a natural language problem into code. We find that code prompting exhibits a high-performance boost for multiple LLMs. Our analysis of GPT 3.5 reveals that the code formatting of the input problem is essential for performance improvement.
  arXiv  Detail & Related papers  (2024-01-18T15:32:24Z)
• LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models [63.14196038655506]
  We introduce LogicAsker, a novel approach for evaluating and enhancing the logical reasoning capabilities of large language models (LLMs) Our methodology reveals significant gaps in LLMs' learning of logical rules, with identified reasoning failures ranging from 29% to 90% across different models. We leverage these findings to construct targeted demonstration examples and fine-tune data, notably enhancing logical reasoning in models like GPT-4o by up to 5%.
  arXiv  Detail & Related papers  (2024-01-01T13:53:53Z)
• ConDefects: A New Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair [22.342625625700908]
  "ConDefects" is a novel dataset of real faults meticulously curated to eliminate such overlap. "ConDefects" contains 1,254 Java faulty programs and 1,625 Python faulty programs. We pair each fault with fault locations and the corresponding repaired code versions, making it tailored for fault localization and program repair related research.
  arXiv  Detail & Related papers  (2023-10-25T00:06:02Z)
• Knowledge-Augmented Language Model Verification [68.6099592486075]
  Recent Language Models (LMs) have shown impressive capabilities in generating texts with the knowledge internalized in parameters. We propose to verify the output and the knowledge of the knowledge-augmented LMs with a separate verifier. Our results show that the proposed verifier effectively identifies retrieval and generation errors, allowing LMs to provide more factually correct outputs.
  arXiv  Detail & Related papers  (2023-10-19T15:40:00Z)
• Large Language Models for Test-Free Fault Localization [11.080712737595174]
  We propose a language model based fault localization approach that locates buggy lines of code without any test coverage information. We fine-tune language models with 350 million, 6 billion, and 16 billion parameters on small, manually curated corpora of buggy programs. Our empirical evaluation shows that LLMAO improves the Top-1 results over the state-of-the-art machine learning fault localization (MLFL) baselines by 2.3%-54.4%, and Top-5 results by 14.4%-35.6%.
  arXiv  Detail & Related papers  (2023-10-03T01:26:39Z)
• A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization [12.80414941523501]
  AutoFL generates an explanation of the bug along with a suggested fault location. Experiments on 798 real-world bugs in Java and Python reveal AutoFL improves method-level acc@1 by up to 233.3% over baselines.
  arXiv  Detail & Related papers  (2023-08-10T10:26:55Z)
• Fault-Aware Neural Code Rankers [64.41888054066861]
  We propose fault-aware neural code rankers that can predict the correctness of a sampled program without executing it. Our fault-aware rankers can significantly increase the pass@1 accuracy of various code generation models.
  arXiv  Detail & Related papers  (2022-06-04T22:01:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.