Bugs in Large Language Models Generated Code: An Empirical Study

• URL: http://arxiv.org/abs/2403.08937v2
• Date: Mon, 18 Mar 2024 14:34:13 GMT
• Title: Bugs in Large Language Models Generated Code: An Empirical Study
• Authors: Florian Tambon, Arghavan Moradi Dakhel, Amin Nikanjam, Foutse Khomh, Michel C. Desmarais, Giuliano Antoniol,
• Abstract summary: Large Language Models (LLMs) for code have gained significant attention recently. Similar to human-written code, LLM-generated code is prone to bugs. This paper examines a sample of 333 bugs collected from code generated using three leading LLMs.
• Score: 12.625305075672456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) for code have gained significant attention recently. They can generate code in different programming languages based on provided prompts, fulfilling a long-lasting dream in Software Engineering (SE), i.e., automatic code generation. Similar to human-written code, LLM-generated code is prone to bugs, and these bugs have not yet been thoroughly examined by the community. Given the increasing adoption of LLM-based code generation tools (e.g., GitHub Copilot) in SE activities, it is critical to understand the characteristics of bugs contained in code generated by LLMs. This paper examines a sample of 333 bugs collected from code generated using three leading LLMs (i.e., CodeGen, PanGu-Coder, and Codex) and identifies the following 10 distinctive bug patterns: Misinterpretations, Syntax Error, Silly Mistake, Prompt-biased code, Missing Corner Case, Wrong Input Type, Hallucinated Object, Wrong Attribute, Incomplete Generation, and Non-Prompted Consideration. The bug patterns are presented in the form of a taxonomy. The identified bug patterns are validated using an online survey with 34 LLM practitioners and researchers. The surveyed participants generally asserted the significance and prevalence of the bug patterns. Researchers and practitioners can leverage these findings to develop effective quality assurance techniques for LLM-generated code. This study sheds light on the distinctive characteristics of LLM-generated code.

Related papers

• 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)
• Where Do Large Language Models Fail When Generating Code? [10.519984835232359]
  Large Language Models (LLMs) have shown great potential in code generation. It is unclear what kinds of code generation errors LLMs can make. We analyzed incorrect code snippets generated by six popular LLMs on the HumanEval dataset.
  arXiv  Detail & Related papers  (2024-06-13T01:29:52Z)
• VersiCode: Towards Version-controllable Code Generation [58.82709231906735]
  Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development. We propose two novel tasks aimed at bridging this gap: version-specific code completion (VSCC) and version-aware code migration (VACM) We conduct an extensive evaluation on VersiCode, which reveals that version-controllable code generation is indeed a significant challenge.
  arXiv  Detail & Related papers  (2024-06-11T16:15:06Z)
• Chain of Targeted Verification Questions to Improve the Reliability of Code Generated by LLMs [10.510325069289324]
  We propose a self-refinement method aimed at improving the reliability of code generated by LLMs. Our approach is based on targeted Verification Questions (VQs) to identify potential bugs within the initial code. Our method attempts to repair these potential bugs by re-prompting the LLM with the targeted VQs and the initial code.
  arXiv  Detail & Related papers  (2024-05-22T19:02:50Z)
• CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification [73.66920648926161]
  We introduce the concept of code hallucinations and propose a classification method for code hallucination based on execution verification. We present a dynamic detection algorithm called CodeHalu designed to detect and quantify code hallucinations. We also introduce the CodeHaluEval benchmark, which includes 8,883 samples from 699 tasks, to systematically and quantitatively evaluate code hallucinations.
  arXiv  Detail & Related papers  (2024-04-30T23:56:38Z)
• InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models [56.723509505549536]
  InfiBench is the first large-scale freeform question-answering (QA) benchmark for code to our knowledge. It comprises 234 carefully selected high-quality Stack Overflow questions that span across 15 programming languages. We conduct a systematic evaluation for over 100 latest code LLMs on InfiBench, leading to a series of novel and insightful findings.
  arXiv  Detail & Related papers  (2024-03-11T02:06:30Z)
• Assured LLM-Based Software Engineering [51.003878077888686]
  This paper is an outline of the content of the keynote by Mark Harman at the International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering, Monday 15th April 2024, Lisbon, Portugal.
  arXiv  Detail & Related papers  (2024-02-06T20:38:46Z)
• 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)
• Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generation [8.575560293086289]
  Large language models (LLMs) have shown extraordinary ability in understanding natural language and generating programming code. The misuse of APIs in the generated code could lead to severe problem, such as resource leaks, program crashes.
  arXiv  Detail & Related papers  (2023-08-20T18:36:28Z)
• Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code? [10.249771123421432]
  We investigate whether Large Language Models (LLMs) attend to the same parts of a task description as human programmers during code generation. We manually analyzed 211 incorrect code snippets and found five attention patterns that can be used to explain many code generation errors. Our findings highlight the need for human-aligned LLMs for better interpretability and programmer trust.
  arXiv  Detail & Related papers  (2023-06-02T00:57:03Z)

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.