The business case for automated software engineering
Abstract
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.
BibTeX
@inproceedings{Menzies-al:ASE07,
author = {Tim Menzies and
Oussama El{-}Rawas and
Jairus Hihn and
Martin S. Feather and
Raymond J. Madachy and
Barry W. Boehm},
title = {The business case for automated software engineering},
booktitle = {ASE},
pages = {303--312},
publisher = {{ACM}},
year = {2007},
}