JPL Technical Report Server

The business case for automated software engineering

Show simple item record Menzies, Tim Elrawas, Oussama Hihn, Jairus M. Feather, Martin S. Madachy, Ray Boehm, Barry 2009-07-23T14:53:00Z 2009-07-23T14:53:00Z 2007-11-05
dc.identifier.citation 22nd IEEE/ACM Automated Software Engineering Conference, Atlanta, Georgia, November 5, 2007. en_US
dc.identifier.clearanceno 07-3331
dc.description.abstract Adoption of advanced automated SE (ASE) tools would be more favored if a business case could be made that these tools are more valuable than alternate methods. In theory, software prediction models can be used to make that case. In practice, this is complicated by the "local tuning" problem. Normally. predictors for software effort and defects and threat use local data to tune their predictions. Such local tuning data is often unavailable. This paper shows that assessing the relative merits of different SE methods need not require precise local tunings. STAR 1 is a simulated annealer plus a Bayesian post-processor that explores the space of possible local tunings within software prediction models. STAR 1 ranks project decisions by their effects on effort and defects and threats. In experiments with NASA systems. STARI found one project where ASE were essential for mmimizing effort/ defect/ threats; and another project were ASE tools were merely optional. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2007. en_US
dc.subject machine learning
dc.subject model evaluation
dc.subject effort estimation
dc.subject COCOMO
dc.title The business case for automated software engineering en_US
dc.type Preprint en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record



My Account