A Deep Learning-Based Method for Identifying User Story Semantic Conflicts.

User stories are a common way of expressing requirements in agile software development. Generally, user stories are written by customers or users in their roles to describe their needs for the to-be system. Since user stories are written by different stakeholders and are refined and modified as agile software versions evolve, there may be conflicts between different user stories. There are many conflicts in user stories, such as duplication and inconsistency between each other. Some of the inconsistencies in user stories are the use of negative words, antonyms, or inconsistencies in the description logic of user story behavior. We call these inconsistencies semantic conflicts. These semantic conflicts make it difficult for agile development teams to understand requirements and make development plans. This paper summarizes two types of semantic conflicts and proposes a deep learn-based approach to identify semantic conflicts between user stories. Through the evaluation of the user story dataset on 9060, the experimental results show that our model has a 95.25% precision, 90.11 % recall and 92.18% F1-Score. Using this automated semantic conflict identification method will help agile team quickly find semantic conflicts between user stories and improve the quality of agile requirements development.