Llama3SP: A resource-Efficient large language model for agile story point estimation

• Fine-tuned LLaMA 3.2 model using QLoRA for Agile story point estimation • Matches GPT2SP accuracy on a 6GB GPU with reduced resource requirements • Maintains high estimation accuracy while lowering training cost • Outperforms prior models in several within- and cross-project evaluations • First application of QLoRA in low-resource Agile effort estimation scenarios Effort estimation remains a major challenge in Agile software development. Inaccurate story point forecasts can lead to budget overruns, schedule delays, and diminished stakeholder trust. Widely used approaches, such as story point estimation, are helpful for planning but rely heavily on subjective human judgment, making them prone to inconsistency and bias. Prior efforts applying machine learning and natural language processing (e.g. Deep-SE, GPT2SP) to automate story point prediction have achieved only limited success, often suffering from accuracy issues, poor cross-project adaptability, and high computational costs. To address these challenges, we introduce Llama3SP , which fine-tunes Meta’s LLaMA 3.2 language model using QLoRA, a resource-efficient adaptation technique. This combination enables training of a high-performance model on standard GPUs without sacrificing prediction quality. Experiments show that Llama3SP provides precise and consistent story point estimates, outperforming or matching previous models like GPT2SP and other comparably sized alternatives, all while operating under significantly lower hardware constraints. These findings highlight how combining advanced NLP models with efficient training techniques can make accurate effort estimation more accessible and practical for agile teams.