Adoption of advanced automated SE (ASE) tools would be 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. STAR1 is a simulated annealer plus a Bayesian post-processor that explores  the space of possible local tunings  within software prediction models. STAR1 ranks project decisions by their effects on effort and defects and threats. In experiments with two NASA systems, STAR1 found that ASE tools were necessary to minimize effort/ defect/ threats.
['Tim Menzies', 'Oussama El-Rawas', 'Jairus Hihn', 'Martin S. Feather', 'Raymond J. Madachy', 'Barry W. Boehm']
The business case for automated software engineering