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Measuring similarity of software designs using graph matching for CBR

Measuring similarity of software designs using graph matching for CBR

Wolf, Markus and Petridis, Miltiadis (2008) Measuring similarity of software designs using graph matching for CBR. In: Proceedings of the Artificial Intelligence Techniques in Software Engineering Workshop (AISEW 2008). IOS Press, pp. 16-20.

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Abstract

This paper examines different ways of measuring similarity between software design models for Case Based Reasoning (CBR) to facilitate reuse of software design and code. The paper considers structural and behavioural aspects of similarity between software design models. Similarity metrics for comparing static class structures are defined and discussed. A Graph representation of UML class diagrams and corresponding similarity measures for UML class diagrams are defined. A full search graph matching algorithm for measuring structural similarity diagrams based on the identification of the Maximum Common Sub-graph (MCS) is presented. Finally, a simple evaluation of the approach is presented and discussed.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the Artificial Intelligence Techniques in Software Engineering Workshop (AISEW 2008)
Additional Information: [1] This paper was presented at the Artificial Intelligence Techniques in Software Engineering Workshop (AISEW 2008), which took place on 21 July 2008, and alongside the 18th European Conference on Artificial Intelligence, held 21-25 July 2008, in Patras, Greece.
Uncontrolled Keywords: software design, Case Based Reasoning (CBR)
Subjects: Q Science > QA Mathematics > QA76 Computer software
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Computer & Computational Science Research Group
School of Computing & Mathematical Sciences > Department of Computer Science
School of Computing & Mathematical Sciences > Department of Computer Systems Technology
Related URLs:
Last Modified: 14 Oct 2016 09:03
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/1261

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