期刊
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
卷 39, 期 6, 页码 822-834出版社
IEEE COMPUTER SOC
DOI: 10.1109/TSE.2012.83
关键词
Data mining; clustering; defect prediction; effort estimation
资金
- US National Science Foundation (NSF) [CCF: 1017330, CCF: 1017263]
- Qatar/WVU [NPRP 09-12-5-2-470]
- Finnish Funding Agency for Technology and Innovation (TEKES)
- Academy of Finland [260871]
- US Army Research Laboratory [W911QX-10-C-0066]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1017263] Funding Source: National Science Foundation
- Academy of Finland (AKA) [260871, 260871] Funding Source: Academy of Finland (AKA)
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.
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