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Title: The business case for automated software engineering
Authors: Menzies, Tim
Elrawas, Oussama
Hihn, Jairus M.
Feather, Martin S.
Madachy, Ray
Boehm, Barry
Keywords: machine learning
model evaluation
effort estimation
Issue Date: 5-Nov-2007
Publisher: Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2007.
Citation: 22nd IEEE/ACM Automated Software Engineering Conference, Atlanta, Georgia, November 5, 2007.
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.
Appears in Collections:JPL TRS 1992+

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