Abstract
Requirement elicitation is very difficult process in highly challenging and business based software as well as in real time software. Common problems associated with these types of software are rapidly changing the requirements and understanding the language of the layman person. In this study, a framework for requirement elicitation by using knowledge based system is proposed, which is very helpful for knowledge documentation, intelligent decision support, self-learning and more specifically it is very helpful for case based reasoning and explanation. Basically in this method requirements are gathered from Artificial Intelligence (AI) expert system from various sources e.g., via interviews, scenarios or use cases. Then, these are converted into structured natural language using ontology and this new problem/case is put forward to Case Based Reasoning (CBR). CBR based on its previous information having similar requirements combines with new case and suggests a proposed solution. Based on this solution a prototype is developed and delivered to customer. The use of case-based reasoning in requirements elicitation process has greatly reduced the burden and saved time of requirement analyst and results in an effective solution for handling complex or vague requirements during the elicitation process.
Highlights
Requirements elicitation is one of the most crucial phases in the software development life cycle
Different Artificial Intelligence (AI) techniques such as knowledge based system; ontology and case based reasoning are used during requirements elicitation which can minimize the problem of natural language understanding as well as greatly reduce the burden and save the time of requirement analyst
Natural Language is the most critical issue that can be reduced by using ontology, which converts the natural language into structured language
Summary
Requirements elicitation is one of the most crucial phases in the software development life cycle. Many techniques for requirements elicitation have been proposed that try to bridge the gap of understanding between stakeholders and requirement analyst and each has its own pros and cons Another way to solve the problems of requirements elicitation is to add machine-learning capabilities during the software development process. Third category lists the analytical approaches for elicitation of requirements In these techniques instead of gathering information from user or their work place, requirements are obtained by similar problems already solved, as described in Cybulski and Reed (2000). Such methods are helpful to get a clear understanding of the application domain. Detailed description of the proposed method is described in later section
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