R2Code: A Self-Reflective LLM Framework for Requirements-to-Code Traceability.
Abstract
Accurate requirement-to-code traceability is crucial for software maintenance. However, existing IR- and embeddingbased methods are heavily dependent on lexical similarity, often yielding incomplete or inconsistent links across projects and languages and incurring high cost from long-context retrieval and prompting. This paper presents R2Code, an LLM-based semantic traceability framework designed to improve trace link accuracy while reducing inference cost. R2Code integrates three components: 1) a decomposition-enhanced Bidirectional Alignment Network (BAN) that aligns four-layer requirement semantics with corresponding code structures to support cross-level semantic matching; 2) a Self-Reflective Consistency Verification (SRCV) module that conducts explanation-guided consistency checking to calibrate link reliability; and 3) a Dynamic Context-Adaptive Retrieval (DCAR) mechanism that adjusts retrieval granularity and filters contexts using semantic-overlap weighting for efficient context utilization. Experiments on five public datasets spanning multiple domains and two programming languages demonstrate that R2Code consistently outperforms the strongest baselines, achieving an average F1 gain of 7.4%, while reducing token consumption by up to 41.7% through adaptive context control. Index Terms-Requirements traceability, LLMs, Semantic alignment, Self-reflection, Context-adaptive retrieval. I. I NTRODUCTION Requirements-to-Code Traceability maintains explicit links among heterogeneous software artifacts such as requirements, design documents, source code, and test cases [1]. It links high-level, free-text documentation (e.g., requirements and design documents) to the source code elements that implement those requirements [2]. Such links support change impact analysis and program comprehension, and they influence the effort required to maintain and evolve software systems [3]. In practice, because traceability links are often missing or were not consistently recorded, an analyst may start from a naturallanguage requirements document and retrieve a ranked list of * Corresponding author: Yishu Li. code elements that are likely to implement it. However, maintaining