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Local vs. global models for effort estimation and defect prediction
Found in: Automated Software Engineering, International Conference on
By Tim Menzies,Andrew Butcher,Andrian Marcus,Thomas Zimmermann,David Cok
Issue Date:November 2011
pp. 343-351
Data miners can infer rules showing how to improve either (a) the effort estimates of a project or (b) the defect predictions of a software module. Such studies often exhibit conclusion instability regarding what is the most effective action for different ...
 
PEASOUP: preventing exploits against software of uncertain provenance (position paper)
Found in: Proceeding of the 7th international workshop on Software engineering for secure systems (SESS '11)
By Anh Nguyen-Tuong, Brian Mastropietro, Chengyu Song, David Cok, David Hyde, David Melski, Denis Gopan, Jack W. Davidson, Jason D. Hiser, John C. Knight, Michele Co, Thomas Bracewell, Wenke Lee
Issue Date:May 2011
pp. 43-49
Because software provides much of the critical services for modern society, it is vitally important to provide methodologies and tools for building and deploying reliable software. While there have been many advances towards this goal, much research remain...
     
Local versus Global Lessons for Defect Prediction and Effort Estimation
Found in: IEEE Transactions on Software Engineering
By Tim Menzies,Andrew Butcher,David Cok,Andrian Marcus,Lucas Layman,Forrest Shull,Burak Turhan,Thomas Zimmermann
Issue Date:June 2013
pp. 822-834
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 v...
 
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