Enhancing the Non-Functional Quality Compliance of LLM-Generated Code through Quality-Aware Preference Learning

[2503.09020] Enhancing the Non-Functional Quality Compliance of LLM-Generated Code through Quality-Aware Preference Learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Software Engineering arXiv:2503.09020 (cs) [Submitted on 12 Mar 2025 ( v1 ), last revised 15 Aug 2026 (this version, v3)] Title: Enhancing the Non-Functional Quality Compliance of LLM-Generated Code through Quality-Aware Preference Learning Authors: Liang Lu , Yuan Jiang , Christoph Treude , Shuzheng Gao , Jingyu Xiao , Xiaohong Su , Michael R. Lyu View a PDF of the paper titled Enhancing the Non-Functional Quality Compliance of LLM-Generated Code through Quality-Aware Preference Learning, by Liang Lu and 5 other authors View PDF HTML (experimental) Abstract: Large Language Models (LLMs) have been widely adopted in commercial code completion engines, significantly enhancing coding efficiency and productivity. However, even functionally correct LLM-generated code may exhibit non-functional quality issues that violate coding standards and best practices, such as poor style and limited maintainability. To address this, we propose a framework for quality-aware preference learning that guides LLMs toward generating criteria-compliant code. Our approach consists of three phases. First, we construct a dataset of paired criteria-violating and criteria-compliant samples, where each pair contains code exhibiting a specific non-functional quality issue and its repaired version that resolves the issue. Second, we design an adaptive token weighting mechanism to emphasize quality-sensitive code regions. Third, we introduce a hybrid optimization objective that combines ranking loss with language modeling loss and KL divergence to enable effective comparative optimization. Extensive experiments on DeepSeek-Coder and Qwen2.5-Coder show that our method substantially improves compliance with the targeted non-functional quality criteria while maintaining functional correctness, achieving a 75.7% relative increase in Quality Reciprocal Score (QRS) on MBPP-sanitized for Qwen2.5-Coder. Fine-tuning a 7B model requires less than three hours, indicating strong practical viability. Ablation studies and a user study further support the effectiveness of the proposed framework. Subjects: Software Engineering (cs.SE) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2503.09020 [cs.SE] (or arXiv:2503.09020v3 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2503.09020 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yuan Jiang [ view email ] [v1] Wed, 12 Mar 2025 03:15:46 UTC (5,860 KB) [v2] Wed, 19 Mar 2025 07:24:48 UTC (7,504 KB) [v3] Sat, 15 Aug 2026 01:29:37 UTC (3,227 KB) Full-text links: Access Paper: View a PDF of the paper titled Enhancing the Non-Functional Quality Compliance of LLM-Generated Code through Quality-Aware Preference Learning, by Liang Lu and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2025-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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