Large Language Model-Based Program Comprehension for Enterprise Software Repositories: A Context-Aware Framework for Code Summarization, Dependency Analysis, and Impact Prediction
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P127Keywords:
Large Language Models, Program Comprehension, Software Repository Mining, Code Summarization, Dependency Analysis, Change Impact Prediction, Retrieval-Augmented Generation, Enterprise Software Engineering, Software Maintenance, Technical DebtAbstract
Enterprise software repositories contain complex, long-lived, and continuously evolving code assets whose behavior is distributed across services, frameworks, configuration files, database schemas, event streams, and deployment scripts. Conventional program comprehension tools provide useful static analysis, search, and dependency visualization capabilities, but they often fail to explain why a code element exists, how a change propagates across enterprise boundaries, and which downstream artifacts are likely to be affected by a modification. Large language models have recently demonstrated strong ability in source code understanding, documentation generation, and natural-language reasoning over program artifacts. However, direct application of generic LLMs to enterprise repositories is insufficient because enterprise comprehension requires contextual grounding, dependency-aware retrieval, architectural constraints, security awareness, and traceable impact prediction. This paper proposes RepoComprehend-LLM, a context-aware framework for LLM-based program comprehension in enterprise software repositories. The framework integrates repository mining, static dependency extraction, semantic code representation, retrieval-augmented generation, graph-based impact propagation, and confidence-calibrated explanation generation. Unlike isolated method-level summarization, the proposed framework generates hierarchical summaries at method, class, service, API, and business-capability levels; constructs multi-layer dependency graphs across code, configuration, database, event, and deployment artifacts; and predicts change impact using structural, semantic, historical, and developer-activity signals. The paper presents the architecture, methodology, scoring functions, evaluation protocol, implementation considerations, and enterprise governance controls for deploying such a framework in regulated software engineering environments. The central contribution is a research-oriented, implementation-ready approach that treats program comprehension as a context-aware, evidence-grounded, and continuously updated intelligence layer for software maintenance, modernization, and release risk assessment.
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