Automatic generation of analysis class diagrams from use case specifications
August 05, 2017 Β· Declared Dead Β· π arXiv.org
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Authors
Jitendra Singh Thakur, Atul Gupta
arXiv ID
1708.01796
Category
cs.SE: Software Engineering
Citations
14
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In object oriented software development, the analysis modeling is concerned with the task of identifying problem level objects along with the relationships between them from software requirements. The software requirements are usually written in some natural language, and the analysis modeling is normally performed by experienced human analysts. The huge gap between the software requirements which are unstructured texts and analysis models which are usually structured UML diagrams, along with human slip-ups inevitably makes the transformation process error prone. The automation of this process can help in reducing the errors in the transformation. In this paper we propose a tool supported approach for automated transformation of use case specifications documented in English language into analysis class diagrams. The approach works in four steps. It first takes the textual specification of a use case as input, and then using a natural language parser generates type dependencies and parts of speech tags for each sentence in the specification. Then, it identifies the sentence structure of each sentence using a set of comprehensive sentence structure rules. Next, it applies a set of transformation rules on the type dependencies and parts of speech tags of the sentences to discover the problem level objects and the relationships between them. Finally, it generates and visualizes the analysis class diagram. We conducted a controlled experiment to compare the correctness, completeness and redundancy of the analysis class diagrams generated by our approach with those generated by the existing automated approaches. The results showed that the analysis class diagrams generated by our approach were more correct, more complete, and less redundant than those generated by the other approaches.
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