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

Authors

Boubou Thiam Niang (ORCID: https://orcid.org/0000-0002-8618-1740)
Benoît Verhaeghe (ORCID: https://orcid.org/0000-0002-4588-2698)
Nicolas Hlad
Anas Shatnawi (ORCID: https://orcid.org/0000-0002-5561-4232)
Ilyes Alili

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.5220/0013570300003964
Primary Topic: Software Testing and Debugging Techniques
Type: article
Field-Weighted Citation Impact: 0.00

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article

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

Boubou Thiam Niang, Benoît Verhaeghe, Nicolas Hlad, Anas Shatnawi et al.

Jan 1, 2025

Software Testing and Debugging Techniques

article

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

Boubou Thiam Niang, Benoît Verhaeghe, Nicolas Hlad, Anas Shatnawi, Ilyes Alili

January 1, 2025article en

Abstract

No abstract available for this paper.

Openalex Percentile: Top 84%

Software Testing and Debugging Techniques

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Enhancing AI-Generated Code Accuracy: Leveraging Model-Based Reverse Engineering for Prompt Context Enrichment — Boubou Thiam Niang, Benoît Verhaeghe, et al. · (2025) | TGRS Research Map | TGRS