We present a framework for learning new analytic BRDF models through Genetic Programming that we call genBRDF. This approach to reflectance modeling can be seen as an extension of traditional methods that rely either on a phenomenological or empirical process. Our technique augments the human effort involved in deriving mathematical expressions that accurately characterize complex high-dimensional reflectance functions through a large-scale optimization. We present a number of analysis tools and data visualization techniques that are crucial to sifting through the large result sets produced by genBRDF in order to identify fruitful expressions. Additionally, we highlight several new models found by genBRDF that have not previously appeared in the BRDF literature. These new BRDF models are compact and more accurate than current state-of-the-art alternatives.

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).