Abstract
Occam’s Razor is a principle of parsimony for problem solving. It states that among competing hypotheses, the one with the fewest assumptions should be selected. This chapter applies Occam’s Razor to model-based project management. In this style of management, a manager uses models to guide their decisions. Ideally, such models should be supported by empirical data.
This chapter explores the limits to building models from data. Results from AI and data mining show that most data sets support only very simple models. For such data, some minimal modeling (supported by automatic tools) will produce models as good as anything else.
Automatic tools can exploit this “minimal models” effect. Such tools can automatically find very simple and very succinct recommendations about how to change and improve software projects.
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Notes
- 1.
“Entia non sunt multiplicanda praeter necessitate,” which translates to “entities must not be multiplied beyond necessity.”
- 2.
- 3.
For example, if programming language experience (plex) takes the range (vl,l,n,h,vh), then range pruning might ignore all but, for example, h, vh.
- 4.
A greedy search takes the next best idea and applies it. This process stops when the next idea does not improve on everything that has been seen before.
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Menzies, T. (2014). Occam’s Razor and Simple Software Project Management. In: Ruhe, G., Wohlin, C. (eds) Software Project Management in a Changing World. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55035-5_18
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