NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
the business case for automated software engineeringAdoption 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 minimizing effort/ defect/ threats; and another project were ASE tools were merely optional.
Document ID
20090028734
Document Type
Conference Paper
External Source(s)
Authors
Menzies, Tim
(West Virginia State Univ. WV, United States)
Elrawas, Oussama
(West Virginia State Univ. WV, United States)
Hihn, Jairus M.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Feather, Martin S.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Madachy, Ray
(University of Southern California CA, United States)
Boehm, Barry
(University of Southern California CA, United States)
Date Acquired
August 24, 2013
Publication Date
November 5, 2007
Subject Category
Computer Programming and Software
Meeting Information
22nd IEEE/ACM Automated Software Engineering Conference(Atlanta, Georgia)
Distribution Limits
Public
Copyright
Other
Keywords
model evaluation
COCOMO
machine learning
effort estimation