Abstract
At the early stage of software lifecycle, the complexity measurement of UML class diagrams plays an important role in software development, testing and maintenance, and provides guidance for developing high quality software. In order to study which one is better, simple or complex metrics, this paper analyzes and compares four typical metrics of UML class diagrams from experimental software engineering view points. Understandability, analyzability and maintainability were classified and predicted for 27 class diagrams related to a banking system by means of algorithm C5.0 within the famous toolkit Weka. Results suggest that the performance of simple metrics is not inferior to that of complex metrics, in some cases even better than that of complex metrics.
Highlights
More and more complexity measurement of UML class diagrams have been developed in literatures, which play an important role in software development, testing and maintenance, and provide guidance for developing high quality software
Reference [6] validated Zhou’s metric by using twenty-seven UML class diagrams related to bank information systems as material
This paper empirically validated the ability of complexity measurement of UML class diagrams to classify and predicate understandability, analyzability and maintainability
Summary
More and more complexity measurement of UML class diagrams have been developed in literatures, which play an important role in software development, testing and maintenance, and provide guidance for developing high quality software. Among these complexity measurements of UML class diagrams, some only focus on counting respective numbers of attributes, methods and relationships among classes [1], they are simple; the others are based on entropy-distance [2]-[4], they are relatively complicated. The remainder of this paper is organized as follows: following the introduction, Section 2 overviews related complexity measurement of UML class diagrams and typical empirical validation works.
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