Predicting delivery capability in iterative software development

Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Aditya Ghose, John Grundy

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)551-573
Number of pages23
JournalIEEE Transactions on Software Engineering
Volume44
Issue number6
DOIs
Publication statusPublished - Jun 2018
Externally publishedYes

Keywords

  • empirical software engineering
  • iterative software development
  • Mining software engineering repositories

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