Facultad Regional Santa Fe
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Item User Stories identification in software's issues records using natural language processing(V ARGENCON, 2020-12) Peña Veitía, Francisco J.; Roldán, María Luciana; Vegetti, María MarcelaNowadays 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.Item Identification of user stories in software issues records applying pre-trained natural language processing models(8º CONAIISI, 2020-12) Peña, Francisco Javier; Roldán, María Luciana; Vegetti, María MarcelaIn the last decades, agile development methods have been increasingly adopted by the software industry. User stories are one of the primary development artifacts for agile project teams. Issue Management Systems are widely used by software development teams to generate user stories, and organize them in meaningful fragments: epics, themes, and sprints. In addition, these tools enable generating any kind of issues, like bugs, change requests, tasks, etc. The responsibility for correctly categorizing an issue is in the hands of the team members, so it is a task prone to errors and frequently omitted due to lack of time or bad practices. Thus, a current problem is that many issues in projects remain uncategorized or mislabeled. Several studies have shown that it is common to find the uncategorized user stories of a software project in large volumes of issues records maintained by Issue Management Systems. In this work, we present two Neural Network models for text classification that were implemented for the identification of user stories in issue records.