Automatic early defects detection in use case documents
Author(s) -
Shuang Liu,
Jun Sun,
Yang Liu,
Yue Zhang,
Bimlesh Wadhwa,
Jin Song Dong,
Xinyu Wang
Publication year - 2014
Publication title -
singapore management university institutional knowledge (ink) (singapore management university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/2642937.2642969
Subject(s) - computer science , parsing , use case diagram , dependency (uml) , function (biology) , dependency grammar , artificial intelligence , natural language processing , software engineering , data mining , information retrieval , programming language , unified modeling language , software , class diagram , evolutionary biology , biology
Use cases, as the primary techniques in the user requirement analysis, have been widely adopted in the requirement engineering practice. As developed early, use cases also serve as the basis for function requirement development, system design and testing. Errors in the use cases could potentially lead to problems in the system design or implementation. It is thus highly desirable to detect errors in use cases. Automatically analyzing use case documents is challenging primarily because they are written in natural languages. In this work, we aim to achieve automatic defect detection in use case documents by leveraging on advanced parsing techniques. In our approach, we first parse the use case document using dependency parsing techniques. The parsing results of each use case are further processed to form an activity diagram. Lastly, we perform defect detection on the activity diagrams. To evaluate our approach, we have conducted experiments on 200+ real-world as well as academic use cases. The results show the effectiveness of our method.
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