Survey of aspect mining case study software and benchmarks

Abstract

Aspect mining is a growing area of research investigating the effective ways of finding crosscutting concerns in existing non-aspect oriented software. Once found, these concerns can be refactored into aspects, which in turn, reduce the system's complexity and make it easier to understand, maintain, and evolve. There are numerous studies that have defined new aspect mining techniques and used case studies to validate their results. This paper analyzes the aspect mining literature, gives a consolidated list of the case studies used for aspect mining validation, discusses weaknesses and strengths of these techniques, and identifies the base research which used it for validation. Based on this analysis, we conclude that there is a critical lack of standard benchmarks for aspect mining. This makes it difficult for new research to evaluate their techniques' quality through empirical validation and to reliably compare their results against other research.