Large Language Model Critics for Execution-Free Evaluation of Code Changes
- URL: http://arxiv.org/abs/2501.16655v1
- Date: Tue, 28 Jan 2025 02:38:56 GMT
- Title: Large Language Model Critics for Execution-Free Evaluation of Code Changes
- Authors: Aashish Yadavally, Hoan Nguyen, Laurent Callot, Gauthier Guinet,
- Abstract summary: Large language models (LLMs) offer a promising way to automate software engineering tasks.
Existing metrics for evaluating such, mainly build status and occasionally log analysis, are too sparse and limited in providing the information needed to assess the quality of changes made.
In this work, we designed LLM-based critics to derive well-structured and rigorous intermediate/step-level, execution-free evaluation proxies for executability of code changes.
- Score: 5.1973075342632535
- License:
- Abstract: Large language models (LLMs) offer a promising way forward for automating software engineering tasks, such as bug fixes, feature additions, etc., via multi-step LLM-based agentic workflows. However, existing metrics for evaluating such workflows, mainly build status and occasionally log analysis, are too sparse and limited in providing the information needed to assess the quality of changes made. In this work, we designed LLM-based critics to derive well-structured and rigorous intermediate/step-level, execution-free evaluation proxies for repo-level code changes. Importantly, we assume access to the gold test patch for the problem (i.e., reference-aware) to assess both semantics and executability of generated patches. With the gold test patch as a reference, we predict executability of all editing locations with an F1 score of 91.6%, aggregating which, we can predict the build status in 84.8% of the instances in SWE-bench. In particular, such an execution-focused LLM critic outperforms other reference-free and reference-aware LLM critics by 38.9% to 72.5%. Moreover, we demonstrate the usefulness of such a reference-aware framework in comparing patches generated by different agentic workflows. Finally, we open-source the library developed for this project, which allows further usage for either other agentic workflows or other benchmarks. The source code is available at https://github.com/amazon-science/code-agent-eval.
Related papers
- A Real-World Benchmark for Evaluating Fine-Grained Issue Solving Capabilities of Large Language Models [11.087034068992653]
FAUN-Eval is a benchmark specifically designed to evaluate the Fine-grAined issUe solviNg capabilities of LLMs.
It is constructed using a dataset curated from 30 well-known GitHub repositories.
We evaluate ten LLMs with FAUN-Eval, including four closed-source and six open-source models.
arXiv Detail & Related papers (2024-11-27T03:25:44Z) - DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation [48.11754113512047]
This study includes a code generation benchmark dataset DOMAINEVAL, encompassing six popular domains.
Our pipeline works in a fully automated manner, enabling a push-bottom construction from code repositories into formatted subjects under study.
The contributions of this study include a code generation benchmark dataset DOMAINEVAL, encompassing six popular domains, a fully automated pipeline for constructing code benchmarks, and an identification of the limitations of LLMs in code generation tasks based on their performance on DOMAINEVAL.
arXiv Detail & Related papers (2024-08-23T16:33:58Z) - Automating Patch Set Generation from Code Review Comments Using Large Language Models [2.045040820541428]
We provide code contexts to five popular Large Language Models (LLMs)
We obtain the suggested code-changes (patch sets) derived from real-world code-review comments.
The performance of each model is meticulously assessed by comparing their generated patch sets against the historical data of human-generated patch-sets.
arXiv Detail & Related papers (2024-04-10T02:46:08Z) - Can Large Language Models be Trusted for Evaluation? Scalable
Meta-Evaluation of LLMs as Evaluators via Agent Debate [74.06294042304415]
We propose ScaleEval, an agent-debate-assisted meta-evaluation framework.
We release the code for our framework, which is publicly available on GitHub.
arXiv Detail & Related papers (2024-01-30T07:03:32Z) - AlignBench: Benchmarking Chinese Alignment of Large Language Models [99.24597941555277]
We introduce AlignBench, a comprehensive benchmark for evaluating Chinese Large Language Models' alignment.
We design a human-in-the-loop data curation pipeline, containing eight main categories, 683 real-scenario rooted queries and corresponding human verified references.
For automatic evaluation, our benchmark employs a rule-calibrated multi-dimensional LLM-as-Judgecitezheng2023judging approach with Chain-of-Thought to generate explanations and final ratings.
arXiv Detail & Related papers (2023-11-30T17:41:30Z) - CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark.
In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship.
We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
arXiv Detail & Related papers (2023-11-29T08:29:54Z) - ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks.
To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
arXiv Detail & Related papers (2023-11-16T12:03:21Z) - Evaluating Large Language Models at Evaluating Instruction Following [54.49567482594617]
We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs.
We discover that different evaluators exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement.
arXiv Detail & Related papers (2023-10-11T16:38:11Z) - Frustrated with Code Quality Issues? LLMs can Help! [7.67768651817923]
static analysis tools are used in developer to flag code quality issues.
Developers need to spend extra efforts to revise their code to improve code quality based on the tool findings.
We present a tool, CORE (short for COde REvisions) to assist developers in revising code to resolve code quality issues.
arXiv Detail & Related papers (2023-09-22T15:37:07Z)
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.