Sunghun Kim, Thomas Zimmermann, Rahul Premraj, Nicolas Bettenburg, and Shivkumar Shivaji
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 vs. global lessons learned for effort estimation and defect prediction. We applied automated clustering tools to effort and defect data sets 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 (a) ignore any existing local divisions into multiple sources; (b) cluster across all available data; then (c) restrict the learning of lessons to the clusters from other sources that are nearest to the test data.
In Proceedings of the 6th Annual India Software Engineering Conference (ISEC 2013)
Copyright © 2013 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or firstname.lastname@example.org. The definitive version of this paper can be found at ACM’s Digital Library --http://www.acm.org/dl/.
Sunghun Kim, Thomas Zimmermann, Rahul Premraj, Nicolas Bettenburg, and Shivkumar Shivaji. Predicting Method Crashes with Bytecode Operations, Microsoft Research, 10 September 2012.