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🧠 AI🟢 BullishImportance 6/10
Automating the Detection of Requirement Dependencies Using Large Language Models
arXiv – CS AI|Ikram Darif, Feifei Niu, Manel Abdellatif, Lionel C. Briand, Ramesh S., Arun Adiththan||6 views
🤖AI Summary
Researchers developed LEREDD, an LLM-based system that automates the detection of dependencies between software requirements using Retrieval-Augmented Generation and In-Context Learning. The system achieved 93% accuracy in classifying requirement dependencies, significantly outperforming existing baselines with relative gains of over 94% in F1 scores for specific dependency types.
Key Takeaways
- →LEREDD uses LLMs with RAG and ICL to automatically identify complex dependencies between natural language software requirements.
- →The system achieved 93% accuracy and 0.84 F1 score overall, with 0.96 F1 for non-dependent cases.
- →LEREDD showed 94.87% and 105.41% relative gains in F1 scores over baselines for Requires dependency detection.
- →Researchers created an annotated dataset of 813 requirement pairs across three systems to support future research.
- →The approach addresses a critical challenge in software development where requirement dependency detection is often overlooked or done manually.
#llm#requirements-engineering#software-development#rag#in-context-learning#automation#dependency-detection#natural-language-processing
Read Original →via arXiv – CS AI
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