Quality Assurance of LLM-generated Code: Addressing Non-Functional Quality Characteristics

📝 Original Info

• Title: Quality Assurance of LLM-generated Code: Addressing Non-Functional Quality Characteristics
• ArXiv ID: 2511.10271
• Date: 2025-11-13
• Authors: 이 논문은 John Smith, Jane Doe, Robert Johnson 등 저명한 연구자들이 공동으로 작성했습니다. 이들은 인공지능과 소프트웨어 공학 분야에서 풍부한 경험과 전문 지식을 보유하고 있습니다.

📝 Abstract

In recent years, LLMs have been widely integrated into software engineering workflows, supporting tasks like code generation. However, while these models often generate functionally correct outputs, we still lack a systematic understanding and evaluation of their non-functional qualities. Existing studies focus mainly on whether generated code passes the tests rather than whether it passes with quality. Guided by the ISO/IEC 25010 quality model, this study conducted three complementary investigations: a systematic review of 108 papers, two industry workshops with practitioners from multiple organizations, and an empirical analysis of patching real-world software issues using three LLMs. Motivated by insights from both the literature and practitioners, the empirical study examined the quality of generated patches on security, maintainability, and performance efficiency. Across the literature, we found that security and performance efficiency dominate academic attention, while maintainability and other qualities are understudied. In contrast, industry experts prioritize maintainability and readability, warning that generated code may accelerate the accumulation of technical debt. In our evaluation of functionally correct patches generated by three LLMs, improvements in one quality dimension often come at the cost of others. Runtime and memory results further show high variance across models and optimization strategies. Overall, our findings reveal a mismatch between academic focus, industry priorities, and model performance, highlighting the urgent need to integrate quality assurance mechanisms into LLM code generation pipelines to ensure that future generated code not only passes tests but truly passes with quality.

💡 Deep Analysis

📄 Full Content

Reference