Measuring the complexity of class diagrams in reverse engineering

Abstract

Complexity metrics for object‐oriented systems are plentiful. Numerous studies have been undertaken to establish valid and meaningful measures of maintainability as they relate to the static structural characteristics of software. In general, these studies have lacked the empirical validation of their meaning and/or have only succeeded in evaluating partial aspects of the system. In this study we have determined, through limited empirical means, a practical and holistic view by analyzing and comparing the structural characteristics of UML class diagrams as those characteristics relate to or impact maintainability. Class diagrams are composed of three kinds of relation, association, generalization, and aggregation, which make their overall structure difficult to understand. We propose combining these three relations in such a way that enables a comprehensive measure of complexity. Theoretically, this measure is applicable among different class diagrams (including different domains, platforms or systems) to the extent that the measure is widely comparative and context free. Further, this property does not preclude comparison within a specific class diagram (or family) and is therefore very useful in evaluating a given class diagram's strengths and weaknesses. We are reporting empirical results that provide a small measure of validity to enable an objective appraisal of both complexity and maintainability without equating the two. Therefore, to evaluate our structural complexity metric, we determined the level of understandability of the system by measuring the time needed to reverse engineer source code into class diagrams including the number of errors produced while creating the diagram. The number of errors produced offers one indicator of maintainability. The results, as compared with other complexity metrics, indicate that our metric shows promise especially if proven to be scalable. Copyright © 2006 John Wiley & Sons, Ltd.

Automatic resolution of model merging conflicts using quality-based reinforcement learning

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