Dialogue enhanced, machine assisted requirements elicitation
The Requirements Elicitation process often involves extracting valuable information from the wealth of extant domain specific, natural language (NL) data to form the requirements for building the future system. It also requires the collaboration of stakeholders from different domains working together to identify additional key information and to clarify any ambiguity from the existing data. However the highly ambiguous and complex nature of natural language is often regarded as the main obstacle preventing effective communication among stakeholders from different domains and therefore success in Requirements Elicitation. Rather than focusing on what can be gathered and/or extracted, this study introduces the concept that detecting what is missing or ambiguous from the domain relevant data represented in natural language can guide stakeholders to provide additional domain relevant information in terms of clarifying any ambiguity. The research was carried out first by a preliminary experiment, using small case studies, involving undergraduate students. This provided the basis of understanding what common mistakes might occur during the process of translating NL descriptions to OO elements. Further investigations were conducted to develop Patterns in assistance of ambiguity detection from NL domain descriptions and Question Templates that can support user clarification. Overall the method of investigation can be summarized as test-build-test, which has been proven to be effective and efficient for this study. This study proves the claim that it is possible to bridge the knowledge gap between non-technical and technical stakeholders by linguistic based Patterns and it also demonstrates that non-technical stakeholders can provide valuable information from a technical stakeholder’s point of view. However, this research can only be treated as a preliminary study. For the concept to work effectively in the real world, a more comprehensive repertoire of Patterns and Question Templates needs to be developed to generate more quality outcomes, in terms of both correctness and completeness of requirements.