A stratification and sampling model for bellwether moving window

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
PublisherKnowledge Systems Institute Graduate School
Pages481-486
ISBN (Print)1891706411
Publication statusPublished - 2017

Publication series

Name
ISSN (Print)2325-9000
ISSN (Electronic)2325-9086

Conference

Title29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017
LocationWyndham Pittsburgh University Center
PlaceUnited States
CityPittsburgh
Period5 - 7 July 2017

Abstract

An effective method for finding the relevant number (window size) and the elapsed time (window age) of recently completed projects has proven elusive in software effort estimation. Although these two parameters significantly affect the
prediction accuracy, there is no effective method to stratify and sample chronological projects to improve prediction performance of software effort estimation models. Exemplary projects (Bellwether) representing the training set have been empirically validated to improve the prediction accuracy in the domain of software defect prediction. However, the concept of Bellwether and its effect have not been empirically proven in software effort estimation as a method of selecting exemplary/relevant projects with defined window size and age. In view of this, we introduce a novel method for selecting relevant and recently completed projects referred to as Bellwether moving window for improving the software effort prediction accuracy. We first sort and cluster a pool of N projects and apply statistical stratification based on Markov chain modeling to select the Bellwether moving window. We evaluate the proposed approach using the baseline Automatically Transformed Linear Model on the ISBSG dataset. Results show that (1) Bellwether effect exist in software effort estimation dataset, (2) the Bellwether moving window with a window size of 82 to 84 projects and window age of 1.5 to 2 years resulted in an improved prediction accuracy than the traditional approach.

Research Area(s)

  • Bellwether effect, Chronological dataset, Markov chains, Window age, Window size

Citation Format(s)

A stratification and sampling model for bellwether moving window. / Mensah, Solomon; Keung, Jacky; Bosu, Michael; Bennin, Kwabena; Kudjo, Patrick Kwaku.

Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE. Knowledge Systems Institute Graduate School, 2017. p. 481-486.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)