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2017, Volume 18, Issue 8

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Frontiers of Information Technology & Electronic Engineering >> 2017, Volume 18, Issue 8 doi: 10.1631/FITEE.1500373

Improved binary similarity measures for software modularization

. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Malaysia.. Department of Computer Science, Quaid-i-Azam University, Islamabad 45320, Pakistan.. Division of Computer Science and Mathematics, University of Stirling, Stirling FK9 4LA, UK.. Department of Computer Science, University of Peshawar, Peshawar 25120, Pakistan.. 5Faculty of Computer and Information Systems, Islamic University Madina, Madina POBox 170, KSA

Available online: 2017-10-31
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Abstract

Various binary similarity measures have been employed in clustering approaches to make homogeneous groups of similar entities in the data. These similarity measures are mostly based only on the presence or absence of features. Binary similarity measures have also been explored with different clustering approaches (e.g., agglomerative hierarchical clustering) for software modularization to make software systems understandable and manageable. Each similarity measure has its own strengths and weaknesses which improve and deteriorate the clustering results, respectively. We highlight the strengths of some well-known existing binary similarity measures for software modularization. Furthermore, based on these existing similarity measures, we introduce several improved new binary similarity measures. Proofs of the correctness with illustration and a series of experiments are presented to evaluate the effectiveness of our new binary similarity measures.

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