AI-generated code poses hidden risks beyond simple bugs, study warns
A major study comparing how software engineers and AI systems evaluate code quality reveals a dangerous gap: developers worry most about messy, hard-to-maintain code that builds technical debt, while research focuses narrowly on security. The finding suggests companies deploying AI coding assistants may be accumulating long-term costs they aren't measuring.
Originaltitel: Quality assurance of LLM-generated code: Addressing non-functional quality characteristics
In recent years, large language models have been widely integrated into software engineering workflows, supporting tasks like code generation. While prior evaluations focus on functional correctness, there is still a limited understanding of the non-functional quality characteristics of generated code. Guided by the ISO/IEC 25010 quality model, this study adopts a multi-methods approach comprising three complementary elements: a literature review of 109 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 regarding security, maintainability, and performance efficiency, which were identified as critical code-level quality attributes. Our results indicate that existing research primarily emphasizes security, performance efficiency, and maintainability, while other quality attributes are understudied. In contrast, practitioners prioritize maintainability and readability, warning that generated code may accelerate the accumulation of technical debt. The empirical evaluation demonstrates the instability of optimizing NFQCs through prompts in practical software engineering settings. Overall, our findings expose a misalignment between academic focus, industry priorities, and observed model behavior, highlighting the 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.