A Novel Framework for Software Project Effort Estimation using LLMs and RAG

Accurate software project effort estimation remains a persistent challenge due to the limitations of traditional models in capturing rich contextual information from project artifacts. Conventional approaches such as COCOMO II and machine learning models primarily rely on structured numerical features, often ignoring the semantic depth present in Software Requirements Specification (SRS) documents. This gap leads to suboptimal estimation performance, particularly in early project stages. To address this limitation, this paper proposes a novel framework that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) for context-aware software effort estimation. The proposed system leverages SRS documents to extract structured attributes, converts historical project data into semantic embeddings, and retrieves similar past projects using vector similarity search. These retrieved analogues are then utilized by a reasoning-based LLM to generate effort predictions grounded in both contextual and historical evidence. The framework is evaluated on the ISBSG dataset using both traditional error metrics and RAG-specific evaluation measures. Experimental results demonstrate that the proposed approach achieves an MMRE of 10.74%, outperforming baseline models including COCOMO II [4], Random Forest, and Support Vector Regression under identical preprocessing settings. Additional analysis using RAGAS metrics shows high context precision (0.9760) and competitive answer correctness (0.7020), validating the effectiveness of the retrieval-generation pipeline. The results highlight the potential of combining LLMs with retrieval mechanisms to bridge the gap between unstructured requirements and quantitative estimation, offering a scalable and interpretable solution for modern software project planning.