![[Paper Reading]: R2Code: A Self-Reflective LLM Framework for Requirements-to-Code Traceability](https://images.gosomo.app/events/27a38849-fbb6-4f9c-ac64-92edc0bbbba7/20a7f9fc-47ec-40c9-ab95-352ef9c214b7_1080.webp)
[Paper Reading]: R2Code: A Self-Reflective LLM Framework for Requirements-to-Code Traceability
📄 Weekly Paper Reading Session
Join SupportVectors AI Training Lab for another deep-dive into cutting-edge AI research! This week's session walks through and discusses the paper:
🔬 R2Code: A Self-Reflective LLM Framework for Requirements-to-Code Traceability
Speaker: Krishnan Ramaswamy
🧠 About the Paper
Accurate requirement-to-code traceability is a cornerstone of effective software maintenance — yet current IR- and embedding-based approaches lean heavily on lexical similarity, frequently producing incomplete or inconsistent trace links across different projects and programming languages, while also incurring significant costs from long-context retrieval and prompting.
This paper introduces R2Code, an LLM-based semantic traceability framework engineered to boost trace link accuracy while simultaneously cutting inference costs. The framework is built around three core components:
- 🔗 Bidirectional Alignment Network (BAN) — A decomposition-enhanced module that aligns four-layer requirement semantics with corresponding code structures, enabling robust cross-level semantic matching
- ✅ Self-Reflective Consistency Verification (SRCV) — An explanation-guided consistency-checking module that calibrates the reliability of trace links
- ⚡ Dynamic Context-Adaptive Retrieval (DCAR) — A mechanism that adjusts retrieval granularity and filters contexts via semantic-overlap weighting for efficient context utilization
📊 Key Results
Experiments conducted across five public datasets spanning multiple domains and two programming languages show that R2Code consistently surpasses the strongest baselines — achieving an average F1 gain of 7.4% while slashing token consumption by up to 41.7% through adaptive context control.
📍 Attendance Options
This session is open to everyone — attend in person at SupportVectors in Fremont, CA (conveniently located near Tesla and accessible via road and BART), or join remotely via Zoom.
🏫 About SupportVectors
SupportVectors AI Training Lab delivers high-quality, hands-on, industry-targeted AI training designed to keep professionals at the forefront of the field. Everyone is welcome — whether you're a seasoned ML engineer or just getting started with AI research! 🚀
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