Improving Quality of Use Case Documents through Learning and User Interaction

Use cases are widely used to capture user requirements based on interactions between different roles in the system. They are mostly documented in natural language and sometimes aided with graphical illustrations in the form of use case diagrams. Use cases serve as an important means to communicate among stakeholders, requirement engineers and system engineers as they are easy to understand and are produced early in the software development process. Having high quality use cases are beneficial in many ways, e.g., in avoiding inconsistency/incompleteness in requirements, in guiding system design, in generating test cases. In this work, we propose an approach to improve the quality of use cases using techniques including natural language processing and machine learning. The central idea is to discover potential problems in use cases through active learning and human interaction and provide feedbacks in natural language. We conduct user studies with a real-world use case document. The results show that our method is helpful in improving use cases with a reasonable amount of user interaction.