一句话：通过 ING 案例的大规模日志数据，识别影响用户故事延迟的团队因素，并构建预测延迟概率与时长的模型。
敏捷开发中团队结构与工作量估算对按时交付至关重要，但团队变化带来的动态及其对交付延迟的影响尚不清楚。本文在 ING 开展案例研究，分析 765,200 条用户故事和 571 个团队的历史日志，识别出刻画延迟用户故事的团队因素，并基于这些因素构建预测延迟可能性与延迟时长的模型。评估显示，引入团队相关特征显著提升延迟预测，平均精确率 74%-82%、召回率 78%-86%、F-measure 76%-84%；团队特征尤其有助于预测延迟可能性，而延迟时长可仅由团队特征解释。采用滑动窗口在近期用户故事上训练可进一步提升预测性能，且对稳定团队效果更好。
In agile software development, proper team structures and effort estimates are crucial to ensure the on-time delivery of software projects. Delivery performance can vary due to the influence of changes in teams, resulting in team dynamics that remain largely unexplored. In this paper, we explore the effects of various aspects of teamwork on delays in software deliveries. We conducted a case study at ING and analyzed historical log data from 765,200 user stories and 571 teams to identify team factors characterizing delayed user stories. Based on these factors, we built models to predict the likelihood and duration of delays in user stories. The evaluation results show that the use of team-related features leads to a significant improvement in the predictions of delay, achieving on average 74%-82% precision, 78%-86% recall and 76%-84% F-measure. Moreover, our results show that team-related features can help improve the prediction of delay likelihood, while delay duration can be explained exclusively using them. Finally, training on recent user stories using a sliding window setting improves the predictive performance; our predictive models perform significantly better for teams that have been stable. Overall, our results indicate that planning in agile development settings can be significantly improved by incorporating team-related information and incremental learning methods into analysis/predictive models.
@inproceedings{paperbot6579,
title = {Modeling Team Dynamics for the Characterization and Prediction of Delays in User Stories},
author = {Elvan Kula and Arie van Deursen and Georgios Gousios},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678939}
}