Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2016

Abstract

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.

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings if the 21st International Conference on Engineering of Complex Computer Systems, Dubai, United Arab Emirates, November 6-8

First Page

101

Last Page

110

Identifier

10.1109/ICECCS.2016.33

Publisher

IEEE

City or Country

United Arab Emirates

Additional URL

https://doi.org/10.1109/ICECCS.2016.33

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