research-article

Predicting Delivery Capability in Iterative Software Development

Authors:

Morakot Choetkiertikul,

John GrundyAuthors Info & Claims

IEEE Transactions on Software Engineering, Volume 44, Issue 6

Pages 551 - 573

https://doi.org/10.1109/TSE.2017.2693989

Published: 01 June 2018 Publication History

Abstract

Iterative software development has become widely practiced in industry. Since modern software projects require fast, incremental delivery for every iteration of software development, it is essential to monitor the execution of an iteration, and foresee a capability to deliver quality products as the iteration progresses. This paper presents a novel, data-driven approach to providing automated support for project managers and other decision makers in predicting delivery capability for an ongoing iteration. Our approach leverages a history of project iterations and associated issues, and in particular, we extract characteristics of previous iterations and their issues in the form of features. In addition, our approach characterizes an iteration using a novel combination of techniques including feature aggregation statistics, automatic feature learning using the Bag-of-Words approach, and graph-based complexity measures. An extensive evaluation of the technique on five large open source projects demonstrates that our predictive models outperform three common baseline methods in Normalized Mean Absolute Error and are highly accurate in predicting the outcome of an ongoing iteration.

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Predicting Delivery Capability in Iterative Software Development

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cover image IEEE Transactions on Software Engineering

IEEE Transactions on Software Engineering Volume 44, Issue 6

June 2018

101 pages

ISSN:0098-5589

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0098-5589 © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.

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Ayala CGómez CManzano MAbherve AFranch X(2024)Forecasting software indicators: an industry-academia collaborationEmpirical Software Engineering10.1007/s10664-024-10508-x29:6Online publication date: 23-Sep-2024
https://dl.acm.org/doi/10.1007/s10664-024-10508-x
Kula EGreuter Evan Deursen AGousios GChandra SBlincoe KTonella P(2023)Dynamic Prediction of Delays in Software Projects using Delay Patterns and Bayesian ModelingProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616328(1012-1023)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616328
Tawosi VMoussa RSarro F(2023)Agile Effort Estimation: Have We Solved the Problem Yet? Insights From a Replication StudyIEEE Transactions on Software Engineering10.1109/TSE.2022.322873949:4(2677-2697)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TSE.2022.3228739
Pasuksmit JThongtanunam PKarunasekera SLo DMcIntosh SNovielli N(2022)Towards reliable agile iterative planning via predicting documentation changes of work itemsProceedings of the 19th International Conference on Mining Software Repositories10.1145/3524842.3528445(35-47)Online publication date: 23-May-2022
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Pasuksmit JSpinellis DGousios GChechik MDi Penta M(2021)Investigating documented information for accurate effort estimation in agile software developmentProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3473106(1605-1609)Online publication date: 20-Aug-2021
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