Dynamic Prediction of Delays in Software Projects using Delay Patterns and Bayesian Modeling

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Abstract

Modern agile software projects are subject to constant change, making it essential to re-asses overall delay risk throughout the project life cycle. Existing effort estimation models are static and not able to incorporate changes occurring during project execution. In this paper, we propose a dynamic model for continuously predicting overall delay using delay patterns and Bayesian modeling. The model incorporates the context of the project phase and learns from changes in team performance over time. We apply the approach to real-world data from 4,040 epics and 270 teams at ING. An empirical evaluation of our approach and comparison to the state-of-the-art demonstrate significant improvements in predictive accuracy. The dynamic model consistently outperforms static approaches and the state-of-the-art, even during early project phases.
Original languageEnglish
Title of host publicationESEC/FSE 2023 - Proceedings of the 31st ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Subtitle of host publicationProceedings of the 2023 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
EditorsSatish Chandra, Kelly Blincoe, Paolo Tonella
Place of PublicationNew York
PublisherACM DL
Pages1012–1023
Number of pages12
ISBN (Electronic)979-8-4007-0327-0
DOIs
Publication statusPublished - 2023
Event31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering - San Francisco, United States
Duration: 3 Dec 20239 Dec 2023

Publication series

NameESEC/FSE 2023 - Proceedings of the 31st ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Conference

Conference31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Abbreviated titleESEC/FSE '23
Country/TerritoryUnited States
Period3/12/239/12/23

Keywords

  • agile methods
  • delay prediction
  • delay patterns
  • bayesian modeling

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