Enhancing AI-Generated Code Accuracy: Leveraging Model-Based Reverse Engineering for Prompt Context Enrichment

Boubou Niang, Ilyes Alili, Benoit Verhaeghe, Nicolas Hlad, Anas Shatnawi

2025

Abstract

Large Language Models (LLMs) have shown considerable promise in automating software development tasks such as code completion, understanding, and generation. However, producing high-quality, contextually relevant code remains a challenge, particularly for complex or domain-specific applications. This paper presents an approach to enhance LLM-based code generation by integrating model-driven reverse engineering to provide richer contextual information. Our findings indicate that incorporating unit tests and method dependencies significantly improves the accuracy and reliability of generated code in industrial projects. In contrast, simpler strategies based on method signatures perform similarly in open-source projects, suggesting that additional context is less critical in such environments. These results underscore the importance of structured input in improving LLM-generated code, particularly for industrial applications.

Paper Citation

in Harvard Style

Niang B., Alili I., Verhaeghe B., Hlad N. and Shatnawi A. (2025). Enhancing AI-Generated Code Accuracy: Leveraging Model-Based Reverse Engineering for Prompt Context Enrichment. In Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-757-3, SciTePress, pages 346-354. DOI: 10.5220/0013570300003964

in Bibtex Style

@conference{icsoft25,
author={Boubou Niang and Ilyes Alili and Benoit Verhaeghe and Nicolas Hlad and Anas Shatnawi},
title={Enhancing AI-Generated Code Accuracy: Leveraging Model-Based Reverse Engineering for Prompt Context Enrichment},
booktitle={Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2025},
pages={346-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013570300003964},
isbn={978-989-758-757-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT
TI - Enhancing AI-Generated Code Accuracy: Leveraging Model-Based Reverse Engineering for Prompt Context Enrichment
SN - 978-989-758-757-3
AU - Niang B.
AU - Alili I.
AU - Verhaeghe B.
AU - Hlad N.
AU - Shatnawi A.
PY - 2025
SP - 346
EP - 354
DO - 10.5220/0013570300003964
PB - SciTePress