A Survey on Evaluating Large Language Models in Code Generation Tasks

• URL: http://arxiv.org/abs/2408.16498v1
• Date: Thu, 29 Aug 2024 12:56:06 GMT
• Title: A Survey on Evaluating Large Language Models in Code Generation Tasks
• Authors: Liguo Chen, Qi Guo, Hongrui Jia, Zhengran Zeng, Xin Wang, Yijiang Xu, Jian Wu, Yidong Wang, Qing Gao, Jindong Wang, Wei Ye, Shikun Zhang,
• Abstract summary: This paper provides a comprehensive review of the current methods and metrics used to evaluate the performance of Large Language Models (LLMs) in code generation tasks. With the rapid growth in demand for automated software development, LLMs have demonstrated significant potential in the field of code generation.
• Score: 30.256255254277914
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper provides a comprehensive review of the current methods and metrics used to evaluate the performance of Large Language Models (LLMs) in code generation tasks. With the rapid growth in demand for automated software development, LLMs have demonstrated significant potential in the field of code generation. The paper begins by reviewing the historical development of LLMs and their applications in code generation. Next, it details various methods and metrics for assessing the code generation capabilities of LLMs, including code correctness, efficiency, readability, and evaluation methods based on expert review and user experience. The paper also evaluates the widely used benchmark datasets, identifying their limitations and proposing directions for future improvements. Specifically, the paper analyzes the performance of code generation models across different tasks by combining multiple evaluation metrics, such as code compilation/interpretation success rates, unit test pass rates, and performance and efficiency metrics, to comprehensively assess the practical application of LLMs in code generation. Finally, the paper discusses the challenges faced in evaluating LLMs in code generation, particularly how to ensure the comprehensiveness and accuracy of evaluation methods and how to adapt to the evolving practices of software development. These analyses and discussions provide valuable insights for further optimizing and improving the application of LLMs in code generation tasks.

Related papers

• A Performance Study of LLM-Generated Code on Leetcode [1.747820331822631]
  This study evaluates the efficiency of code generation by Large Language Models (LLMs) We compare 18 LLMs, considering factors such as model temperature and success rate, and their impact on code performance. We find that LLMs are capable of generating code that is, on average, more efficient than the code written by humans.
  arXiv  Detail & Related papers  (2024-07-31T13:10:03Z)
• 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)
• Towards more realistic evaluation of LLM-based code generation: an experimental study and beyond [36.1669124651617]
  We conduct an empirical study to understand Large Language Models' code generation performance within settings that reflect the evolving nature of software development. We find that previous evolving-ignored evaluation approaches lead to inflated performance of the LLMs, ranging from 10.0% to 61.1%.
  arXiv  Detail & Related papers  (2024-06-11T03:19:18Z)
• A Survey on Large Language Models for Code Generation [9.555952109820392]
  Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks. This survey aims to bridge the gap between academia and practical development by providing a comprehensive and up-to-date literature review.
  arXiv  Detail & Related papers  (2024-06-01T17:48:15Z)
• AI-powered Code Review with LLMs: Early Results [10.37036924997437]
  We present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model. Our proposed LLM-based AI agent model is trained on large code repositories. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code.
  arXiv  Detail & Related papers  (2024-04-29T08:27:50Z)
• Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models [95.96734086126469]
  Large language models (LLMs) can serve as the assistant to help users accomplish their jobs, and also support the development of advanced applications. For the wide application of LLMs, the inference efficiency is an essential concern, which has been widely studied in existing work. We perform a detailed coarse-to-fine analysis of the inference performance of various code libraries.
  arXiv  Detail & Related papers  (2024-04-17T15:57:50Z)
• CodeEditorBench: Evaluating Code Editing Capability of Large Language Models [49.387195629660994]
  Large Language Models (LLMs) for code are rapidly evolving, with code editing emerging as a critical capability. We introduce CodeEditorBench, an evaluation framework designed to rigorously assess the performance of LLMs in code editing tasks. We curate diverse coding challenges and scenarios from five sources, covering various programming languages, complexity levels, and editing tasks.
  arXiv  Detail & Related papers  (2024-04-04T15:49:49Z)
• Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming [12.355284125578342]
  Large Language Models (LLMs) have become a focal point in modern software development. LLMs offer the potential to significantly augment developer productivity by serving as intelligent, chat-driven programming assistants. However, each system requires the LLM to be honed to its set of workspaces to ensure the best performance.
  arXiv  Detail & Related papers  (2024-02-22T03:51:34Z)
• Code Needs Comments: Enhancing Code LLMs with Comment Augmentation [91.52444946362547]
  We introduce a novel data augmentation method that generates comments for existing code, coupled with a data filtering strategy that filters out code data poorly correlated with natural language. We conducted experiments on three code-focused Large Language Models and observed consistent improvements in performance on two widely-used programming skill benchmarks.
  arXiv  Detail & Related papers  (2024-02-20T13:56:38Z)
• A Thorough Examination of Decoding Methods in the Era of LLMs [72.65956436513241]
  Decoding methods play an indispensable role in converting language models from next-token predictors into practical task solvers. This paper provides a comprehensive and multifaceted analysis of various decoding methods within the context of large language models. Our findings reveal that decoding method performance is notably task-dependent and influenced by factors such as alignment, model size, and quantization.
  arXiv  Detail & Related papers  (2024-02-10T11:14:53Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)

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.