Background: There are many data mining methods but few comparisons between them. For example, there are at least two ways to build quality optimizers, programs that find project options that change quality measures like defects, development effort (total staff hours), and time (elapsed calendar months). In the first way, we construct a parametric model to represent prior software projects. In the second way, we just apply case-based reasoning to reason directly from historical cases. Aim: To assess case-based reasoning vs parametric modeling for quality optimization. Method: We compared the W case-based reasoner against the SEEWAW parametric modeling tool. Results: W is easy to explain and fast to build. It makes no parametric assumptions and hence can be rapidly applied to project data in many formats. SEESAW is an elaborate tool that can only process project data expressed in a particular ontology (i.e. just the COCOMO attributes). It is also slower to execute than W . In 24 differe...