Clustering software components for program restructuring and component reuse using hybrid XNOR similarity function.

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Title: Clustering software components for program restructuring and component reuse using hybrid XNOR similarity function.
Authors: Srinivas, Chintakindi1, Radhakrishna, Vangipuram2 radhakrishna_v@vnrvjiet.in, Rao, C. V. Guru3
Source: Interdisciplinarity in Engineering. 2014, Vol. 12, p246-254. 9p.
Subjects: Document clustering, Component software, Data mining, Software engineering, Computer software industry
Abstract: Component based software development has gained a lot of practical importance in the field of software engineering from academic researchers and also from industry perspective. Finding components for efficient software reuse is one of the important problems aimed by researchers. Clustering reduces the search space of components by grouping similar entities together thus ensuring reduced time complexity as it reduces the search time for component retrieval. In this research, we instigate a generalized approach for clustering a given set of documents or software components by defining a similarity function called hybrid XNOR function to find degree of similarity between two document sets or software components. A similarity matrix is obtained for a given set of documents or components by applying hybrid XNOR function. We define and design the algorithm for component or document clustering which has the input as similarity matrix and output being set of clusters. The output is a set of highly cohesive pattern groups or components. [ABSTRACT FROM AUTHOR]
Copyright of Interdisciplinarity in Engineering is the property of University of Medicine, Pharmacy, Sciences and Technology of Tîrgu Mures 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: Component based software development has gained a lot of practical importance in the field of software engineering from academic researchers and also from industry perspective. Finding components for efficient software reuse is one of the important problems aimed by researchers. Clustering reduces the search space of components by grouping similar entities together thus ensuring reduced time complexity as it reduces the search time for component retrieval. In this research, we instigate a generalized approach for clustering a given set of documents or software components by defining a similarity function called hybrid XNOR function to find degree of similarity between two document sets or software components. A similarity matrix is obtained for a given set of documents or components by applying hybrid XNOR function. We define and design the algorithm for component or document clustering which has the input as similarity matrix and output being set of clusters. The output is a set of highly cohesive pattern groups or components. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Interdisciplinarity in Engineering is the property of University of Medicine, Pharmacy, Sciences and Technology of Tîrgu Mures 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: 10.1016/j.protcy.2013.12.482
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        Text: English
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      – SubjectFull: Data mining
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              Text: 2014
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