GPT2SP: A Transformer-Based Agile Story Point Estimation Approach

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Story point estimation is a task to estimate the overall effort required to fully implement a product backlog item. Various estimation approaches (e.g., Planning Poker, Analogy, and expert judgment) are widely-used, yet they are still inaccurate and may be subjective, leading to ineffective sprint planning. Recent work proposed Deep-SE, a deep learning-based Agile story point estimation approach, yet it is still inaccurate, not transferable to other projects, and not interpretable. In this paper, we propose GPT2SP, a Transformer-based Agile Story Point Estimation approach. Our GPT2SP employs a GPT-2 pre-trained language model with a GPT-2 Transformer-based architecture, allowing our GPT2SP models to better capture the relationship among words while considering the context surrounding a given word and its position in the sequence and be transferable to other projects, while being interpretable. Through an extensive evaluation on 23,313 issues that span across 16 open-source software projects with 10 existing baseline approaches for within- and cross-project scenarios, our results show that our GPT2SP approach achieves a median MAE of 1.16, which is (1) 34%-57% more accurate than existing baseline approaches for within-project estimations; (2) 39%-49% more accurate than existing baseline approaches for cross-project estimations. The ablation study also shows that the GPT-2 architecture used in our approach substantially improves Deep-SE by 6%-47%, highlighting the significant advancement of the AI for Agile story point estimation. Finally, we develop a proof-of-concept tool to help practitioners better understand the most important words that contributed to the story point estimation of the given issue with the best supporting examples from past estimates. Our survey study with 16 Agile practitioners shows that the story point estimation task is perceived as an extremely challenging task. In addition, our AI-based story point estimation with explanations is perceived as more useful and trustworthy than without explanations, highlighting the practical need of our Explainable AI-based story point estimation approach.