Tuesday, August 11, 2009

Paper accepted to ASE'09

Understanding the Value of Software Engineering Technologies .

Phillip Green II, Tim Menzies, Steven Williams, Oussama El-Rawas

SEESAW combines AI search tools, a Monte Carlo simulator, and some software process models. We show here that, when selecting technologies for a software project, SEESAW out-performs a variety of other search engines. SEESAW’s recommendations are greatly affected by the business context of its use. For example, the automatic defect reduction tools explored by the ASE community are only relevant to a subset of software projects, and only according to certain value criteria. Therefore, when arguing for the value of a particular technology, that argument should include a description of the value function of the target user community.

Note: ASE has a 17% acceptance rate.

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