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STG/18/060#55067509

Abstract:

User stories describe system requirements from the users’ point of view using structured natural language. These artifacts mostly assist the development team in agile projects where sets of them express the expected features of the to-be software. Nevertheless, when faced with large user stories’ sets, further structuring needs to be introduced to make the software problem more understandable/clear. Indeed, the latter can hardly be understood on the basis of huge flat lists of sentences. The present paper describes the premises of an experiment destined to evaluate whether novice modelers can understand the requirements problem better through a visual representation (called Rationale Tree) built out of a user stories’ set. It does so by comparing, on the same objective, the Rationale Tree method to the industry-adopted User Story Mapping technique with a modeling exercise. This was performed with the participation of two student groups where one used the former technique while the other one employed the latter. The ultimate goal of this exercise is to identify the actual performance of the Rational Tree in delivering understandability to the requirements problem (when input is provided in the form of a complex user stories’ set) in order to identify possible bottlenecks and improvement opportunities within the method itself.