一句话：提出可复用的端到端流水线，以静态分析评估并改进 LLM 生成代码的质量与安全性。
LLM 正被广泛用于生成生产代码，但对其质量与安全性的系统评估方法仍不成熟。本文（教程）提出一套可复用的端到端评估流水线，根植于经验软件工程实践，聚焦生成后验证而非提示设计。参与者使用静态分析工具评估代码的可维护性、可靠性与安全性，并在不同模型、提示与人类编写的基线之间进行比较；流水线支持对输出进行结构化聚合与解释，使评估可复现且有依据。扩展方向包括智能体修复、用于信任校准的可解释性以及偏见感知评估。参与者将获得实用评估工件和在现代开发流程中验证 AI 生成代码的原则性框架。
Large Language Models (LLMs) are increasingly used to generate production code, yet systematic methods for evaluating their quality and security remain underdeveloped. This tutorial introduces a reusable, end-to-end evaluation pipeline grounded in empirical software engineering practices, focusing on post-generation validation rather than prompt design. Participants will apply static analysis tools to assess maintainability, reliability, and security, and compare results across models, prompts, and human-written baselines. The pipeline supports structured aggregation and interpretation of outputs, enabling reproducible and defensible assessments. Extensions include agentic remediation, explainability for trust calibration, and bias-aware evaluation. Attendees will leave with practical evaluation artifacts and a principled framework for validating AI-generated code in modern development workflows.
@inproceedings{MeloPCYCA26,
title = {Evaluating and Improving the Quality of LLM-Generated Code},
author = {Glaucia Melo and Jessica Pourleyli and Genevieve Caumartin and Corey Yang-Smith and Diego Elias Costa and Ahmad Abdellatif},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3804900}
}