Retrieval-Augmented Software Engineering for Enterprise Codebases: Context-Aware Requirement Traceability, Bug Localization, and Developer Decision Support
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P130Keywords:
Retrieval-Augmented Generation, Software Engineering, Requirement Traceability, Bug Localization, Enterprise Codebases, Code Intelligence, Developer Decision Support, Software Governance, Hybrid Retrieval, Explainable AIAbstract
Enterprise software engineering increasingly operates under conditions of scale, regulatory exposure, architectural fragmentation, and accelerated delivery pressure. Large codebases are distributed across services, repositories, branches, build pipelines, requirements systems, incident records, design documents, and operational telemetry. In this setting, developers and engineering leaders must answer high-stakes questions: which code implements a requirement, where a defect is likely to be fixed, which architectural dependency explains a failure, and what decision is justified by available engineering evidence. This paper proposes RASE, a retrieval-augmented software engineering framework for context-aware requirement traceability, bug localization, and developer decision support in enterprise codebases. RASE treats software engineering artifacts as a governed, typed, versioned, and auditable knowledge substrate rather than a passive document collection. The framework combines hybrid lexical–dense retrieval, code-aware embeddings, dependency graphs, evidence ranking, retrieval-conditioned generation, and decision governance. Unlike generic retrieval-augmented generation pipelines, RASE introduces artifact-type aware chunking, traceability-preserving metadata, multi-hop retrieval over code and non-code artifacts, and evidence contracts that constrain generated recommendations to retrieved, inspectable sources. The paper presents a research design, architecture, operational workflow, and reproducible evaluation protocol for three core tasks: recovering requirement-to-code links, localizing buggy files from reports and incidents, and supporting developer decisions during design, change impact analysis, and release governance. The proposed evaluation emphasizes recall-oriented retrieval quality, developer-facing usefulness, provenance precision, latency, security, and organizational adoption. The contribution is a research-grade framework that integrates established information retrieval principles, modern code representation learning, secure software development governance, and AI-enabled lifecycle management into a unified enterprise software engineering method. RASE is positioned as an empirical artifact for future controlled industrial validation, with clearly defined research questions, hypotheses, metrics, ablation strategy, and threats to validity.
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