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A Granular Hierarchical Multiview Metrics Suite for Statecharts Quality.

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Title: A Granular Hierarchical Multiview Metrics Suite for Statecharts Quality.
Authors: Beldjehem, Mokhtar1 mbeldjeh@uottawa.ca
Source: Advances in Software Engineering. 2013, p1-13. 13p.
Subjects: Statecharts (Computer science), Computer software quality control, Topology, Fuzzy systems, Computer systems, Problem solving
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. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Software Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Statecharts+%28Computer+science%29%22">Statecharts (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+quality+control%22">Computer software quality control</searchLink><br /><searchLink fieldCode="DE" term="%22Topology%22">Topology</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+systems%22">Fuzzy systems</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+systems%22">Computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Advances in Software Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/2013/952178
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        Text: English
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        PageCount: 13
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      – SubjectFull: Statecharts (Computer science)
        Type: general
      – SubjectFull: Computer software quality control
        Type: general
      – SubjectFull: Topology
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      – SubjectFull: Fuzzy systems
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      – SubjectFull: Problem solving
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              Text: 2013
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