EA-Analyzer: automating conflict detection in a large set of textual aspect-oriented requirements

摘要

One of the aims of Aspect-Oriented Requirements Engineering is to address the composability and subsequent analysis of crosscutting and non-crosscutting concerns during requirements engineering. A composition definition explicitly represents interdependencies and interactions between concerns. Subsequent analysis of such compositions helps to reveal conflicting dependencies that need to be resolved in requirements. However, detecting conflicts in a large set of textual aspect-oriented requirements is a difficult task as a large number of explicitly defined interdependencies need to be analyzed. This paper presents EA-Analyzer, the first automated tool for identifying conflicts in aspect-oriented requirements specified in natural-language text. The tool is based on a novel application of a Bayesian learning method. We present an empirical evaluation of the tool with three industrial-strength requirements documents from different domains and a fourth academic case study used as a de facto benchmark in several areas of the aspect-oriented community. This evaluation shows that the tool achieves up to 93.90 % accuracy regardless of the documents chosen as the training and validation sets.