User Stories identification in software's issues records using natural language processing

Nowadays most of software development companies have adopted agile development methodologies, which suggest capturing requirements through user stories. The use of these good practices improves the organization of work teams and the quality of the resulting software product. However, user stories are too often poorly written in practice and exhibit inherent quality defects. In addition, it is common to find the user stories of a software project immersed in large volumes of issues request logs from software quality tracking systems, which makes difficult to process them later. In order to solve these defects and to formulate high quality requirements, a current trend is the application of computational linguistic techniques to identify and then process user stories. In this work, we present two recurrent neural network models that were developed for the identification of user stories in issue records from software quality tracking systems for further processing.