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Software evolution research inherently has several resource-intensive logistical constraints. Archived project artifacts, such as those found in source code repositories and bug tracking systems, are the principal source of input data. Analysis-specific facts, such as commit metadata or the location of design patterns within the code, must be extracted for each change or configuration of interest. The results of this resource-intensive "fact extraction" phase must be stored efficiently, for later use by more experimental types of research tasks, such as algorithm or model refinement. In order to perform any type of software evolution research, each of these logistical issues must be addressed and an implementation to manage it created. In this paper, we introduce Kenyon, a system designed to facilitate software evolution research by providing a common set of solutions to these common logistical problems. We have used Kenyon for processing source code data from 12 systems of varying sizes and domains, archived in 3 different types of software configuration management systems. We present our experiences using Kenyon with these systems, and also describe Kenyon's usage by students in a graduate seminar class.
Bevan et al. (Thu,) studied this question.