DocumentCode :
77947
Title :
Local versus Global Lessons for Defect Prediction and Effort Estimation
Author :
Menzies, T. ; Butcher, A. ; Cok, D. ; Marcus, Andrian ; Layman, Lucas ; Shull, Forrest ; Turhan, Burak ; Zimmermann, Thomas
Author_Institution :
Lane Dept. of Comput. Sci. & Electr. Eng., West Virginia Univ., Morgantown, WV, USA
Volume :
39
Issue :
6
fYear :
2013
fDate :
Jun-13
Firstpage :
822
Lastpage :
834
Abstract :
Existing research is unclear on how to generate lessons learned for defect prediction and effort estimation. Should we seek lessons that are global to multiple projects or just local to particular projects? This paper aims to comparatively evaluate local versus global lessons learned for effort estimation and defect prediction. We applied automated clustering tools to effort and defect datasets from the PROMISE repository. Rule learners generated lessons learned from all the data, from local projects, or just from each cluster. The results indicate that the lessons learned after combining small parts of different data sources (i.e., the clusters) were superior to either generalizations formed over all the data or local lessons formed from particular projects. We conclude that when researchers attempt to draw lessons from some historical data source, they should 1) ignore any existing local divisions into multiple sources, 2) cluster across all available data, then 3) restrict the learning of lessons to the clusters from other sources that are nearest to the test data.
Keywords :
automatic test pattern generation; data mining; pattern clustering; PROMISE repository; automated clustering tools; data source; defect dataset; defect prediction; effort estimation; global lessons; learned lesson generated rule; local lessons; Context; Data models; Estimation; Java; Measurement; Software; Telecommunications; Data mining; clustering; defect prediction; effort estimation;
fLanguage :
English
Journal_Title :
Software Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0098-5589
Type :
jour
DOI :
10.1109/TSE.2012.83
Filename :
6363444
Link To Document :
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