| Summary: | Poor requirements analysis process results in incomplete software applications. Some requirements appear as scattered and tangled concerns within requirements document. Hence it is difficult to identify such requirements. A number of research approaches such as Theme/Doc, early aspects identification, information retrieval and aspects identification using UML have been developed to identify crosscutting concern at the requirements level. Nevertheless, these approaches are only supported by semiautomated tools whereby human intervention is required to achieve the desired results. This research focuses on developing a tool to automatically identify crosscutting concern at the requirements level. A model based on Theme/Doc and early aspects identification approaches is formulated as the basis of this tool, 3CI. 3CI adopts natural language processing (NLP) techniques such as verb frequency analysis, part-of-speech tagging and dominant verb analysis. The tool usability, efficiency and scalability are evaluated by comparing the performance of a requirements engineer conducting similar task manually. Our evaluation on 3CI demonstrates 75 of accuracy.
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