Abstract
How can we best find project changes that most improve project estimates? Prior solutions to this problem required the use of standard software process models that may not be relevant to some new project. Also, those prior solutions suffered from limited verification (the only way to assess the results of those studies was to run the recommendations back through the standard process models). Combining case-based reasoning and contrast set learning, the W system requires no underlying model. Hence, it is widely applicable (since there is no need for data to conform to some software process models). Also, W’s results can be verified (using holdout sets). For example, in the experiments reported here, W found changes to projects that greatly reduced estimate median and variance by up to 95% and 83% (respectively).
Highlights
Existing research in effort estimations focuses mostly on deriving estimates from past project data using (e.g.) parametric models [1] or case-based reasoning (CBR) [2] or genetic algorithms [3]
How can we best find project changes that most improve project estimates? Prior solutions to this problem required the use of standard software process models that may not be relevant to some new project
Those prior solutions suffered from limited verification
Summary
Existing research in effort estimations focuses mostly on deriving estimates from past project data using (e.g.) parametric models [1] or case-based reasoning (CBR) [2] or genetic algorithms [3]. [4,5,6,7], we have tackled this problem using STAR/NOVA, a suite of AI search algorithms that explored the input space of standard software process models to find project options that most reduced the effort estimates. This paper explores five data sets with W, three of which cannot be processed using STAR/NOVA. The W extension to CBR is described (contrast set learning over the local neighborhood), using a small example This is followed by fourteen case studies, one with Brooks’ Law, and thirteen others. Our conclusion will discuss when W is preferred over STAR/NOVA
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