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. Composing concerns may help to reveal conflicting dependencies that need to be identified and resolved. However, detecting conflicts in a large set of textual aspect-oriented requirements is an error-prone and time-consuming task. 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 that has been effective at classifying text. We present an empirical evaluation of the tool with three industrial-strength requirements documents from different real-life domains. We show that the tool achieves up to 92.97% accuracy when one of the case study documents is used as a training set and the other two as a validation set.

The goal of aspect-oriented requirements engineering (AORE) is to identify possible crosscutting concerns, and to develop composition specifications around those concerns. These compositions can be used to reason about potential conflicts in the requirements and to relate requirements to architecture in semantically meaningful ways. Recent work in AORE has moved from a syntactic approach to composition, which leads to fragile compositions and increased coupling between aspect and base concerns, to a semantic composition approach, based on semantics of the natural language itself. However, such compositions are at present only informally specified, and as such formal reasoning about the requirements and the subsequent derivations are difficult. We present a formal approach to these semantic-based compositions which facilitates this reasoning. We show that the approach especially lends itself to identifying conflicts between requirements and mapping compositions to a derived architecture.