Transformer-Enhanced Deep Characterization of Sprints in Agile Software Development

DOI

10.2991/978-94-6463-950-6_32How to use a DOI?

Keywords

Deep Learning; Transformer Networks; Agile Development; Sprint Prediction; Software Engineering; Self-Attention; LSTM; Vector Embeddings

Abstract

Agile methodologies, particularly Scrum, have become foundational in modern software development, with sprints serving as time-boxed iterations for incremental delivery. Accurate sprint outcome prediction is essential for effective project management and resource allocation. While Sprint2Vec introduced a deep learning framework using LSTM networks to characterize sprints through vector embeddings, it faces limitations in modeling long-range dependencies due to sequential processing constraints.

This paper presents Sprint2Vec +, which extends Sprint2Vec by integrating transformer-based architectures that leverage self-attention mechanisms to capture complex interactions among sprint elements. We evaluate our approach using datasets from five open-source projects—Apache, Atlassian, Jenkins, Spring, and Talendforge — containing over 5,000 sprints and 71,000 issues. Sprint2Vec + demonstrates a 10.8% improvement in combined prediction accuracy (10.3% for productivity, 11.3% for quality) with 40.9% faster inference time compared to the original LSTM-based approach. Beyond predictive improvements, Sprint2Vec + provides interpretable attention patterns revealing critical relationships between planning activities and implementation outcomes. Statistical significance testing (p < 0.001) confirms the robustness of improvements across all project domains. This work contributes a scalable, transformer-based solution for advancing sprint analytics in Agile software engineering.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Raghu Govind Alvandar
AU  - Pradeepta Mishra
AU  - Shinu Abhi
PY  - 2025
DA  - 2025/12/29
TI  - Transformer-Enhanced Deep Characterization of Sprints in Agile Software Development
BT  - Proceedings of the International Conference on Smart Systems and Social Management (ICSSSM 2025)
PB  - Atlantis Press
SP  - 481
EP  - 499
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-950-6_32
DO  - 10.2991/978-94-6463-950-6_32
ID  - Alvandar2025
ER  -