@inproceedings{10.1145/3803437.3804900,
author = {Melo, Glaucia and Pourleyli, Jessica and Caumartin, Genevieve and Yang-Smith, Corey and Costa, Diego and Abdellatif, Ahmad},
title = {Evaluating and Improving the Quality of LLM-Generated Code},
year = {2026},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3803437.3804900},
doi = {10.1145/3803437.3804900},
abstract = {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.},
booktitle = {Proceedings of the 34th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering: Companion Proceedings},
series = {FSE Companion '26},
location = {Montr\'{e}al, Canada},
numpages = {2},
keywords = {large language models, code quality, software engineering education, static analysis}
}