Reducing Information Asymmetry in Software Product Management: An LLM-Based Reverse Engineering Framework

Although the transition from the Waterfall model to Agile practices has accelerated software delivery, it has often weakened documentation practices, contributing to persistent information asymmetry between Product Managers and Developers. This study introduces an LLM-based reverse engineering framework designed to assist product management workflows by analyzing source code and generating enriched development tickets. The proposed Interactive Product Management Assistant leverages the long-context capabilities of Gemini 1.5 Pro together with a context-caching mechanism to analyze large codebases, identify ambiguities in product requests, highlight potential edge cases, detect possible cascading dependencies (“domino effects”), and generate code pointers that guide developers to relevant implementation areas. The framework was evaluated through case studies on several open-source projects, including WordPress, ERPNext, Ghost, and Odoo. The results suggest that the system can support requirement clarification, improve visibility of potential implementation impacts, and reduce exploratory effort during code analysis. In addition, the implemented preprocessing and caching mechanisms reduce analysis costs and improve operational efficiency during iterative interactions. Rather than providing a large-scale quantitative before-and-after comparison, this paper presents a qualitative case study and a proof-of-concept implementation to demonstrate the feasibility of the proposed approach. Overall, the findings demonstrate the feasibility of using LLM-assisted reverse engineering to support requirements analysis and product–developer collaboration, highlighting the potential of AI-based tools to complement traditional requirements engineering practices in complex software projects.