Towards optimal project planning : a unified framework for story point and effort estimation in agile software development

Abstract

The persistent challenge of accurate story point and effort estimation in Agile Software Development stems from the inherent subjectivity and variability of conventional approaches such as Planning Poker and expert judgement. This investigation tackles these fundamental limitations through the development of a hybrid deep learning architecture that combines LSTM networks, CNN layers, and Transformer encoder blocks, aiming to support a more systematic, data-driven estimation methodology in place of purely subjective, human-driven approaches. The proposed approach leverages multi-task learning paradigms to concurrently predict both story points and effort estimates, capitalizing on their intrinsic interdependencies to enhance predictive accuracy. The framework implements systematic data preprocessing pipelines and employs a concatenation-based combination of GloVe and Word2Vec embeddings to capture both global and local semantic relationships, while integrated attention mechanisms support transparency and interpretability in estimation decision-making processes. Comprehensive evaluation is conducted using the TAWOS dataset, encompassing 458,232 issues across 39 diverse open-source projects. The LSTM-CNN-Transformer methodology is rigorously compared against established traditional techniques and previously proposed AI approaches from the literature under a unified experimental protocol, employing a consistent suite of evaluation metrics, including MAE, MedAE, accuracy, F1-scores, and MMRE. Across the majority of projects, the proposed model matches or outperforms competing methods for both story point classification and effort prediction, demonstrating robust and generalisable performance rather than isolated improvements on a few favourable cases. This research makes several contributions to both theoretical understanding and practical implementation in software estimation, delivering a comprehensive data-driven solution that reduces subjectivity whilst preserving interpretability and ensuring practical applicability for Agile development teams.