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Measuring Object-Oriented Class Cohesion Based on Complex Networks

Class cohesion has an immediate impact on maintainability, modifiability and understandability of the software. Here, a new metric of cohesion based on complex networks (CBCN) for measuring connectivity of class members was developed mainly relying on calculating class average clustering coefficient... Full description

Journal Title: Arabian Journal for Science and Engineering 2017, Vol.42(8), pp.3551-3561
Main Author: Gu, Aihua
Other Authors: Zhou, Xiaofeng , Li, Zonghua , Li, Qinfeng , Li, Lu
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 2193-567X ; E-ISSN: 2191-4281 ; DOI: 10.1007/s13369-017-2588-x
Link: http://dx.doi.org/10.1007/s13369-017-2588-x
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recordid: springer_jour10.1007/s13369-017-2588-x
title: Measuring Object-Oriented Class Cohesion Based on Complex Networks
format: Article
creator:
  • Gu, Aihua
  • Zhou, Xiaofeng
  • Li, Zonghua
  • Li, Qinfeng
  • Li, Lu
subjects:
  • Object-oriented class
  • Software quality
  • Class cohesion metric
  • Complex networks
  • Software metrics
ispartof: Arabian Journal for Science and Engineering, 2017, Vol.42(8), pp.3551-3561
description: Class cohesion has an immediate impact on maintainability, modifiability and understandability of the software. Here, a new metric of cohesion based on complex networks (CBCN) for measuring connectivity of class members was developed mainly relying on calculating class average clustering coefficient from graphs representing connectivity patterns of the various class members. In addition, the CBCN metric was assessed with theoretical validation according to four properties (nonnegativity and normalization, null and maximum values, monotonicity, cohesive modules) of the class cohesion theory. Based on data comparison with existing seventeen typical class cohesion metrics of class cohesion for a system, the CBCN metric was superior to others. Applying the CBCN metric to three open source software systems to calculate class average clustering coefficients, we found that understanding, modification and maintenance of classes in an open software system could be likely less difficult compared with those of others. Three open software systems have power-law distributions for the class average clustering coefficient, which makes possible the further understanding of the cohesion metric based on complex networks.
language: eng
source:
identifier: ISSN: 2193-567X ; E-ISSN: 2191-4281 ; DOI: 10.1007/s13369-017-2588-x
fulltext: fulltext
issn:
  • 2191-4281
  • 21914281
  • 2193-567X
  • 2193567X
url: Link


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subjectObject-oriented class ; Software quality ; Class cohesion metric ; Complex networks ; Software metrics
descriptionClass cohesion has an immediate impact on maintainability, modifiability and understandability of the software. Here, a new metric of cohesion based on complex networks (CBCN) for measuring connectivity of class members was developed mainly relying on calculating class average clustering coefficient from graphs representing connectivity patterns of the various class members. In addition, the CBCN metric was assessed with theoretical validation according to four properties (nonnegativity and normalization, null and maximum values, monotonicity, cohesive modules) of the class cohesion theory. Based on data comparison with existing seventeen typical class cohesion metrics of class cohesion for a system, the CBCN metric was superior to others. Applying the CBCN metric to three open source software systems to calculate class average clustering coefficients, we found that understanding, modification and maintenance of classes in an open software system could be likely less difficult compared with those of others. Three open software systems have power-law distributions for the class average clustering coefficient, which makes possible the further understanding of the cohesion metric based on complex networks.
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titleMeasuring Object-Oriented Class Cohesion Based on Complex Networks
descriptionClass cohesion has an immediate impact on maintainability, modifiability and understandability of the software. Here, a new metric of cohesion based on complex networks (CBCN) for measuring connectivity of class members was developed mainly relying on calculating class average clustering coefficient from graphs representing connectivity patterns of the various class members. In addition, the CBCN metric was assessed with theoretical validation according to four properties (nonnegativity and normalization, null and maximum values, monotonicity, cohesive modules) of the class cohesion theory. Based on data comparison with existing seventeen typical class cohesion metrics of class cohesion for a system, the CBCN metric was superior to others. Applying the CBCN metric to three open source software systems to calculate class average clustering coefficients, we found that understanding, modification and maintenance of classes in an open software system could be likely less difficult compared with those of others. Three open software systems have power-law distributions for the class average clustering coefficient, which makes possible the further understanding of the cohesion metric based on complex networks.
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abstractClass cohesion has an immediate impact on maintainability, modifiability and understandability of the software. Here, a new metric of cohesion based on complex networks (CBCN) for measuring connectivity of class members was developed mainly relying on calculating class average clustering coefficient from graphs representing connectivity patterns of the various class members. In addition, the CBCN metric was assessed with theoretical validation according to four properties (nonnegativity and normalization, null and maximum values, monotonicity, cohesive modules) of the class cohesion theory. Based on data comparison with existing seventeen typical class cohesion metrics of class cohesion for a system, the CBCN metric was superior to others. Applying the CBCN metric to three open source software systems to calculate class average clustering coefficients, we found that understanding, modification and maintenance of classes in an open software system could be likely less difficult compared with those of others. Three open software systems have power-law distributions for the class average clustering coefficient, which makes possible the further understanding of the cohesion metric based on complex networks.
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