A Construct for Assessing and Improving the Quality of Agile User Stories
Open AccessProject Managers are increasingly choosing the Agile methodology for software development projects. In contrast to traditional requirements, Agile user stories are a technique of creating easily understandable, user centric software requirements in an easy and flexible way. The quality of requirements affects all subsequent phases of the Software Development Life Cycle (SDLC). Requirements which are poorly written, unclear, ambiguous, or incorrect contribute to 50% of all system defects (Vaz, 2018) . Moreover, defect correction costs increase as much as 50–200 times in later phases than when corrected during the Requirements Engineering (RE) phase (Boehm & Papaccio, 1988). However, Agile practitioners often struggle to write quality user stories and then assess the quality of their user stories. Consideration must be given to providing methods and tools to improve the quality of Agile requirements without compromising agility, schedule, cost, or risk. This research contributes to Agile Requirements Engineering by creating a construct to assess and improve user story quality using Controlled Natural Language (CNL) and Natural Language Processing (NLP) techniques. The results of this research provide Agile practitioners with key factors for assessing user story quality in addition to showing how CNL can be used to improve quality. Additionally, this research introduces a novel NLP tool – StoryLine - which automatically improves user story quality using the INVEST criteria and the Quality User Story (QUS) framework (Lucassen, Dalpiaz, Werf, et al., 2016). Unlike existing tools, StoryLine increases productivity and efficiency in time-sensitive environments by accurately completing the tedious tasks of ensuring all requirements are singular, clear, consistent, and in a common format.
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