LLM-as-a-Judge for Reference-less Automatic Code Validation and Refinement for Natural Language to Bash in IT Automation

• URL: http://arxiv.org/abs/2506.11237v1
• Date: Thu, 12 Jun 2025 19:15:05 GMT
• Title: LLM-as-a-Judge for Reference-less Automatic Code Validation and Refinement for Natural Language to Bash in IT Automation
• Authors: Ngoc Phuoc An Vo, Brent Paulovicks, Vadim Sheinin,
• Abstract summary: It is crucial to verify if the generated code for remediation action is syntactically and semantically correct.<n>In this work, we focused on enhancing LLM-as-a-Judge using bidirectional functionality matching and logic representation.<n>Results show high accuracy and agreement with execution-based evaluation.
• Score: 0.9176056742068815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In an effort to automatically evaluate and select the best model and improve code quality for automatic incident remediation in IT Automation, it is crucial to verify if the generated code for remediation action is syntactically and semantically correct and whether it can be executed correctly as intended. There are three approaches: 1) conventional methods use surface form similarity metrics (token match, exact match, etc.) which have numerous limitations, 2) execution-based evaluation focuses more on code functionality based on pass/fail judgments for given test-cases, and 3) LLM-as-a-Judge employs LLMs for automated evaluation to judge if it is a correct answer for a given problem based on pre-defined metrics. In this work, we focused on enhancing LLM-as-a-Judge using bidirectional functionality matching and logic representation for reference-less automatic validation and refinement for Bash code generation to select the best model for automatic incident remediation in IT Automation. We used execution-based evaluation as ground-truth to evaluate our LLM-as-a-Judge metrics. Results show high accuracy and agreement with execution-based evaluation (and up to 8% over baseline). Finally, we built Reflection code agents to utilize judgments and feedback from our evaluation metrics which achieved significant improvement (up to 24% increase in accuracy) for automatic code refinement.

Related papers

• Automated Validation of LLM-based Evaluators for Software Engineering Artifacts [0.7548538278943616]
  REFINE (Ranking Evaluators for FIne grained Nuanced Evaluation) is an automated framework for benchmarking large language models (LLMs)<n> REFINE applies novel generation techniques to automatically synthesize artifacts with progressively reduced quality.<n>It quantifies each candidate evaluator configuration by measuring how closely its rankings align with expected ordering.
  arXiv  Detail & Related papers  (2025-08-04T18:52:01Z)
• Automated Code Review Using Large Language Models with Symbolic Reasoning [0.0]
  This study proposes a hybrid approach that integrates symbolic reasoning techniques with Large Language Models.<n>Our results show that this approach improves the accuracy and efficiency of automated code review.
  arXiv  Detail & Related papers  (2025-07-24T14:50:27Z)
• CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks [63.562924932512765]
  Large Language Models (LLMs) have advanced the state-of-the-art in various coding tasks.<n>LLMs can also serve as judges, assessing and comparing the quality of responses generated by other models.
  arXiv  Detail & Related papers  (2025-07-14T17:56:29Z)
• BitsAI-CR: Automated Code Review via LLM in Practice [16.569842114384233]
  BitsAI-CR is an innovative framework that enhances code review through a two-stage approach.<n>System is built upon a comprehensive taxonomy of review rules and implements a data flywheel mechanism.<n> Empirical evaluation demonstrates BitsAI-CR's effectiveness, achieving 75.0% precision in review comment generation.
  arXiv  Detail & Related papers  (2025-01-25T08:39:50Z)
• Re-evaluating Automatic LLM System Ranking for Alignment with Human Preference [63.03859517284341]
  An automatic evaluation framework aims to rank LLMs based on their alignment with human preferences.<n>An automatic LLM bencher consists of four components: the input set, the evaluation model, the evaluation type and the aggregation method.
  arXiv  Detail & Related papers  (2024-12-31T17:46:51Z)
• Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models [54.14602121129874]
  We introduce AutoIF, the first scalable and reliable method for automatically generating instruction-following training data. AutoIF transforms the validation of instruction-following data quality into code verification.
  arXiv  Detail & Related papers  (2024-06-19T13:29:53Z)
• 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)
• How Reliable Are Automatic Evaluation Methods for Instruction-Tuned LLMs? [3.1706553206969925]
  We perform a meta-evaluation of such methods and assess their reliability across a broad range of tasks. We observe that while automatic evaluation methods can approximate human ratings under specific conditions, their validity is highly context-dependent. Our findings enhance the understanding of how automatic methods should be applied and interpreted when developing and evaluating instruction-tuned LLMs.
  arXiv  Detail & Related papers  (2024-02-16T15:48:33Z)
• 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)
• Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers [121.53749383203792]
  We present a holistic end-to-end solution for annotating the factuality of large language models (LLMs)-generated responses. We construct an open-domain document-level factuality benchmark in three-level granularity: claim, sentence and document. Preliminary experiments show that FacTool, FactScore and Perplexity are struggling to identify false claims.
  arXiv  Detail & Related papers  (2023-11-15T14:41:57Z)

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.