Evaluating Source Code Quality with Large Languagem Models: a comparative study

• URL: http://arxiv.org/abs/2408.07082v2
• Date: Sun, 22 Sep 2024 15:05:25 GMT
• Title: Evaluating Source Code Quality with Large Languagem Models: a comparative study
• Authors: Igor Regis da Silva Simões, Elaine Venson,
• Abstract summary: This paper describes and analyzes the results obtained using Large Language Model (LLM) as a static analysis tool. A total of 1,641 classes were analyzed, comparing the results in two versions of models: GPT 3.5 Turbo and GPT 4o. The GPT 4o version did not present the same results, diverging from the previous model and Sonar by assigning a high classification to codes that were assessed as lower quality.
• Score: 2.3204178451683264
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Code quality is an attribute composed of various metrics, such as complexity, readability, testability, interoperability, reusability, and the use of good or bad practices, among others. Static code analysis tools aim to measure a set of attributes to assess code quality. However, some quality attributes can only be measured by humans in code review activities, readability being an example. Given their natural language text processing capability, we hypothesize that a Large Language Model (LLM) could evaluate the quality of code, including attributes currently not automatable. This paper aims to describe and analyze the results obtained using LLMs as a static analysis tool, evaluating the overall quality of code. We compared the LLM with the results obtained with the SonarQube software and its Maintainability metric for two Open Source Software (OSS) Java projects, one with Maintainability Rating A and the other B. A total of 1,641 classes were analyzed, comparing the results in two versions of models: GPT 3.5 Turbo and GPT 4o. We demonstrated that the GPT 3.5 Turbo LLM has the ability to evaluate code quality, showing a correlation with Sonar's metrics. However, there are specific aspects that differ in what the LLM measures compared to SonarQube. The GPT 4o version did not present the same results, diverging from the previous model and Sonar by assigning a high classification to codes that were assessed as lower quality. This study demonstrates the potential of LLMs in evaluating code quality. However, further research is necessary to investigate limitations such as LLM's cost, variability of outputs and explore quality characteristics not measured by traditional static analysis tools.

Related papers

• AIME: AI System Optimization via Multiple LLM Evaluators [79.03422337674664]
  AIME is an evaluation protocol that utilizes multiple LLMs that each independently generate an evaluation on separate criteria and then combine them via concatenation. We show AIME outperforming baseline methods in code generation tasks, with up to $62%$ higher error detection rate and up to $16%$ higher success rate than a single LLM evaluation protocol on LeetCodeHard and HumanEval datasets.
  arXiv  Detail & Related papers  (2024-10-04T04:03:24Z)
• TestBench: Evaluating Class-Level Test Case Generation Capability of Large Language Models [8.22619177301814]
  We introduce TestBench, a benchmark for class-level LLM-based test case generation. We construct a dataset of 108 Java programs from 9 real-world, large-scale projects on GitHub. We propose a fine-grained evaluation framework that considers five aspects of test cases: syntactic correctness, compilation correctness, test correctness, code coverage rate, and defect detection rate.
  arXiv  Detail & Related papers  (2024-09-26T06:18:06Z)
• Source Code Summarization in the Era of Large Language Models [23.715005053430957]
  Large language models (LLMs) have led to a great boost in the performance of code-related tasks. In this paper, we undertake a systematic and comprehensive study on code summarization in the era of LLMs.
  arXiv  Detail & Related papers  (2024-07-09T05:48:42Z)
• RepEval: Effective Text Evaluation with LLM Representation [55.26340302485898]
  RepEval is a metric that leverages the projection of Large Language Models (LLMs) representations for evaluation. Our work underscores the richness of information regarding text quality embedded within LLM representations, offering insights for the development of new metrics.
  arXiv  Detail & Related papers  (2024-04-30T13:50:55Z)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)
• User Centric Evaluation of Code Generation Tools [2.8115477071897788]
  This paper proposes a user centric method for evaluating the usability of large language models (LLMs) It includes metadata in the test cases of a benchmark to describe their usages, conducts testing in a multi-attempt process that mimics the uses of LLMs, measures LLM generated solutions on a set of quality attributes that reflect usability, and evaluates the performance based on user experiences in the uses of LLMs as a tool.
  arXiv  Detail & Related papers  (2024-02-05T15:56:19Z)
• Self-Evaluation Improves Selective Generation in Large Language Models [54.003992911447696]
  We reformulate open-ended generation tasks into token-level prediction tasks. We instruct an LLM to self-evaluate its answers. We benchmark a range of scoring methods based on self-evaluation.
  arXiv  Detail & Related papers  (2023-12-14T19:09:22Z)
• BLESS: Benchmarking Large Language Models on Sentence Simplification [55.461555829492866]
  We present BLESS, a performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS) We assess a total of 44 models, differing in size, architecture, pre-training methods, and accessibility, on three test sets from different domains (Wikipedia, news, and medical) under a few-shot setting. Our evaluation indicates that the best LLMs, despite not being trained on TS, perform comparably with state-of-the-art TS baselines.
  arXiv  Detail & Related papers  (2023-10-24T12:18:17Z)
• Large Language Model-Aware In-Context Learning for Code Generation [75.68709482932903]
  Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation. We propose a novel learning-based selection approach named LAIL (LLM-Aware In-context Learning) for code generation.
  arXiv  Detail & Related papers  (2023-10-15T06:12:58Z)
• Benchmarking Causal Study to Interpret Large Language Models for Source Code [6.301373791541809]
  This paper introduces a benchmarking strategy named Galeras comprised of curated testbeds for three SE tasks. We illustrate the insights of our benchmarking strategy by conducting a case study on the performance of ChatGPT under distinct prompt engineering methods.
  arXiv  Detail & Related papers  (2023-08-23T20:32:12Z)
• Software Code Quality Measurement: Implications from Metric Distributions [6.110201315596897]
  We categorized distinct metrics into two types: 1) monotonic metrics that consistently influence code quality; and 2) non-monotonic metrics that lack a consistent relationship with code quality. Our work contributes to the multi-dimensional construct of code quality and its metric measurements, which provides practical implications for consistent measurements on both monotonic and non-monotonic metrics.
  arXiv  Detail & Related papers  (2023-07-22T13:55:42Z)

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.