Background: Given information on just a few prior projects, how do we learn the best and fewest changes for current projects? Aim: To conduct a case study comparing two ways to recommend project changes. 1) Data farmers use Monte Carlo sampling to survey and summarize the space of possible outcomes. 2) Case-based reasoners (CBR) explore the neighborhood around test instances. Method: We applied a state-of-the data farmer (SEESAW) and a CBR tool ()'V2) to software project data. Results: CBR with )'V2 was more effective than SEESAW's data farming for learning best and recommended project changes, effectively reducing runtime, effort, and defects. Further, CBR with )'V2 was comparably easier to build, maintain, and apply in novel domains, especially on noisy data sets. Conclusion: Use CBR tools like )'V2 when data are scarce or noisy or when project data cannot be expressed in the required form of a data farmer. Future Work: This study applied our own CBR tool to several small data sets. Future work could apply other CBR tools and data farmers to other data (perhaps to explore other goals such as, say, minimizing maintenance effort).

When AI search methods are applied to software process models, then appropriate technologies can be discovered for a software project. We show that those 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.