Predicting likelihood of requirement implementation within the planned iteration: An empirical study at IBM

Ali Dehghan, Adam Neal, Kelly Blincoe, Johan Linaker, Daniela Damian

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

Abstract

There has been a significant interest in the estimation of time and effort in fixing defects among both software practitioners and researchers over the past two decades. However, most of the focus has been on prediction of time and effort in resolving bugs, without much regard to predicting time needed to complete high-level requirements, a critical step in release planning. In this paper, we describe a mixed-method empirical study on three large IBM projects in which we developed and evaluated a process of training a predictive model constituting a set of 29 features in nine categories in order to predict if a requirement will be completed within its planned iteration. We conducted feature engineering through iterative interviews with IBM practitioners as well as analysis of large development repositories of these three projects. Using machine learning techniques, we were able to make predictions on completion time of requirements at four different stages of their lifetime. Using our industrial partner's interest in high precision over recall, we then adopted a cost sensitive learning method and maximized precision of predictions (ranging from 0.8 to 0.97) while maintaining an acceptable recall. We also ranked the features based on their relative importance to the optimized predictive model. We show that although satisfying predictions can be made at early stages, performance of predictions improves over time by taking advantage of requirements' progress data. Furthermore, feature importance ranking results show that although importance of features are highly dependent on project and prediction stage, there are certain features (e.g. requirement creator, time remained to the end of iteration, time since last requirement summary change and number of times requirement has been replanned for a new iteration) that emerge as important across most projects and stages, implying future worthwhile research directions for both researchers and practitioners.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE/ACM 14th International Conference on Mining Software Repositories, MSR 2017
PublisherIEEE Computer Society
Pages124-134
Number of pages11
ISBN (Electronic)9781538615447
DOIs
Publication statusPublished - 2017 Jun 29
Event14th IEEE/ACM International Conference on Mining Software Repositories, MSR 2017 - Buenos Aires, Argentina
Duration: 2017 May 202017 May 21

Conference

Conference14th IEEE/ACM International Conference on Mining Software Repositories, MSR 2017
Country/TerritoryArgentina
CityBuenos Aires
Period2017/05/202017/05/21

Subject classification (UKÄ)

  • Computer Science

Free keywords

  • Completion Time Prediction
  • Machine Learning
  • Mining Software Repositories
  • Release Planning

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