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Abstract

In this article, we present a semi-automated approach for identifying candidate early aspects in requirements specifications. This approach aims at improving the precision of the aspect identification process in use cases, and also solving some problems of existing aspect mining techniques caused by the vagueness and ambiguity of text in natural language. To do so, we apply a combination of text analysis techniques such as: natural language processing (NLP) and word sense disambiguation (WSD). As a result, our approach is able to generate a graph of candidate concerns that crosscut the use cases, as well as a ranking of these concerns according to their importance. The developer then selects which concerns are relevant for his/her domain. Although there are still some challenges, we argue that this approach can be easily integrated into a UML development methodology, leading to improved requirements elicitation.

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