Requirements elicitation and elicitation technique selection: model for two knowledge-intensive software development processes
Ann M. Hickey, A.M. Davis
Abstract
Ann M. Hickey, A.M. Davis
Abstract
By its very nature, software development consists of many knowledge-intensive processes. One of the most difficult to model, however, is requirements elicitation. This paper presents a mathematical model of the requirements elicitation process that clearly shows the critical role of knowledge in its performance. One meta-process of requirements elicitation, selection of an appropriate elicitation technique, is also captured in the model. The values of this model are: (1) improved understanding of what needs to be performed during elicitation helps analysts improve their elicitation efforts, (2) improved understanding of how elicitation techniques are selected helps less experienced analysts be as successful as more experienced analysts, and (3) as we improve our ability to perform elicitation, we improve the likelihood that the systems we create meet their intended customers' needs. Many papers have been written that promulgate specific elicitation methods. A few have been written that model elicitation in general. However, none have yet to model elicitation in a way that makes clear the critical role played by knowledge. This paper's model captures the critical roles played by knowledge in both elicitation and elicitation technique selection.
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By its very nature, software development consists of many knowledge-intensive processes. One of the most difficult to model, however, is requirements elicitation. This paper presents a mathematical model of the requirements elicitation process that clearly shows the critical role of knowledge in its performance. One meta-process of requirements elicitation, selection of an appropriate elicitation technique, is also captured in the model. The values of this model are: (1) improved understanding of what needs to be performed during elicitation helps analysts improve their elicitation efforts, (2) improved understanding of how elicitation techniques are selected helps less experienced analysts be as successful as more experienced analysts, and (3) as we improve our ability to perform elicitation, we improve the likelihood that the systems we create meet their intended customers' needs. Many papers have been written that promulgate specific elicitation methods. A few have been written that model elicitation in general. However, none have yet to model elicitation in a way that makes clear the critical role played by knowledge. This paper's model captures the critical roles played by knowledge in both elicitation and elicitation technique selection.
Key concepts: Expert elicitation, Requirements elicitation, Computer science, Photo elicitation, Process (computing), Selection (genetic algorithm), Knowledge management, Management science