A Survey on User Stories Using NLP for Agile Development
Bodem Niharika, Shivali Chopra · 2024
The team of developers needs to learn as much as possible about the software at the initial phase of the procedure so that they can create a solid strategy. Members of the team should also properly examine any data associated with the change. Estimating how long it will take to create software is a common problem in the software engineering industry and is known as software development effort estimation (SDEE). Each iteration of an agile methodology project delivers a collection of specifications recognized as the product backlog. In agile projects, user stories are commonly used as objects for documenting customer needs. They are relatively brief fragments of writing that have a semi-structured style and demonstrate specific needs. Implementations that make use of user stories could benefit from NLP methods. This paper gives an overview of research in natural language processing studies of product backlogs. The search showed the main research articles about NLP methods in user stories. Most of this research employed natural language processing methods to retrieve (who, what, and why) details from backlogs. The goals of natural language processing research on backlogs are varied and can include finding bugs, creating software artifacts, pinpointing the stories’ central abstractions, and following the threads connecting the frameworks and the stories themselves. Domain experts could use natural language processing to control and manage story points. There are a variety of advantages and disadvantages to integrating NLP into user stories. To produce high-quality studies, researchers need to learn about and experiment with various NLP methods and then subject those results to thorough assessment processes. In the field of natural language processing, context understanding remains a formidable obstacle.