Table of Contents
Advances in Software Engineering
Volume 2013 (2013), Article ID 952178, 13 pages
http://dx.doi.org/10.1155/2013/952178
Research Article

A Granular Hierarchical Multiview Metrics Suite for Statecharts Quality

University of Ottawa, School of Information Technology and Engineering, 800 King Edward, Ottawa, ON, Canada K1N 6N5

Received 30 March 2013; Revised 7 June 2013; Accepted 7 June 2013

Academic Editor: Phillip A. Laplante

Copyright © 2013 Mokhtar Beldjehem. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a bottom-up approach for a multiview measurement of statechart size, topological properties, and internal structural complexity for understandability prediction and assurance purposes. It tackles the problem at different conceptual depths or equivalently at several abstraction levels. The main idea is to study and evaluate a statechart at different levels of granulation corresponding to different conceptual depth levels or levels of details. The higher level corresponds to a flat process view diagram (depth = 0), the adequate upper depth limit is determined by the modelers according to the inherent complexity of the problem under study and the level of detail required for the situation at hand (it corresponds to the all states view). For purposes of measurement, we proceed using bottom-up strategy starting with all state view diagram, identifying and measuring its deepest composite states constituent parts and then gradually collapsing them to obtain the next intermediate view (we decrement depth) while aggregating measures incrementally, until reaching the flat process view diagram. To this goal we first identify, define, and derive a relevant metrics suite useful to predict the level of understandability and other quality aspects of a statechart, and then we propose a fuzzy rule-based system prototype for understandability prediction, assurance, and for validation purposes.