Improving the Process of Evaluating User Stories Using the Paraconsistent Annotated Evidential Logic Eτ

Software developers need to be agile to meet users’ needs, delivering software on tight, quality deadlines. User history is a technique used in agile methods to elicit requirements. However, this process is performed with the developers and the user, and there may be contradictions between them, resulting in inaccurate metrics. This article presents a model of validation of user history using the Para-analyzer algorithm, based on the Paraconsistent Annotated Evidential Logic Eτ to assist in improving the evaluation, prioritization, and estimation process of user stories. A survey was conducted with a team of developers working with agile methods. The model uses the degrees of favourable and contrary evidence for each INVEST criterion as input variables. The application of this model allows considering extremely relevant issues when it comes to supporting decision-making based on a mathematical model and serving as a support tool for teams, Product Owners, Project Managers, and others. Four user stories were analyzed by nine experts, who evaluated the criteria for each user story. The interpretation of the evaluations performed by the experts was through the global analysis in the unit square of the Cartesian plane, which indicated the degrees of favourable evidence and contrary evidence for the data used. Two stories that could not be developed in a Sprint were verified and, therefore, should be refactored and resubmitted to the opinion of experts. The other two stories had favourable evidence to be used in a Sprint.