MDRE-LLM: A Tool for Analyzing and Applying LLMs in Software Reverse Engineering

Understanding and maintaining software systems often requires extracting high-level abstractions, such as domain models, from source code. MDRE-LLM addresses this challenge by integrating Large Language Models (LLMs) with traditional Model-Driven Reverse Engineering (MDRE) techniques, offering an innovative approach to automate and enhance domain model recovery. The tool supports flexible granularity strategies and validates LLM -generated models against deterministic baselines. MDRE-LLM addresses diverse use cases, including analyzing legacy systems with minimal documentation, rapidly compre-hending large-scale codebases, and validating LLM performance in reverse engineering tasks. These capabilities have the potential to improve software analysis and refactoring while advance AI-driven research and education by fostering systematic experimentation and collaboration. The tool and a webcast are available at https://zenodo.org/uploads/14072106.

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Publication Details

Published
2025-03-04
DOI
https://doi.org/10.1109/saner64311.2025.00090
Citations
3
Primary Topic
Software Engineering Research
Type
article
Field-Weighted Citation Impact
9.74
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article

MDRE-LLM: A Tool for Analyzing and Applying LLMs in Software Reverse Engineering

Artur Boronat, Jawad Mustafa
3 citations
Software Engineering Research
9.74
article

MDRE-LLM: A Tool for Analyzing and Applying LLMs in Software Reverse Engineering

Artur Boronat, Jawad Mustafa
article en
3 citations

Abstract

Understanding and maintaining software systems often requires extracting high-level abstractions, such as domain models, from source code. MDRE-LLM addresses this challenge by integrating Large Language Models (LLMs) with traditional Model-Driven Reverse Engineering (MDRE) techniques, offering an innovative approach to automate and enhance domain model recovery. The tool supports flexible granularity strategies and validates LLM -generated models against deterministic baselines. MDRE-LLM addresses diverse use cases, including analyzing legacy systems with minimal documentation, rapidly compre-hending large-scale codebases, and validating LLM performance in reverse engineering tasks. These capabilities have the potential to improve software analysis and refactoring while advance AI-driven research and education by fostering systematic experimentation and collaboration. The tool and a webcast are available at https://zenodo.org/uploads/14072106.

University of Leicester (GB)
Industry, innovation and infrastructure
Openalex Percentile: Top 2%
Software Engineering Research
9.74
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