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Quantifying Class Cohesion in Object‐Oriented Software Systems Using Hesitant Fuzzy Entropy for Improved Software Maintenance.

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Title: Quantifying Class Cohesion in Object‐Oriented Software Systems Using Hesitant Fuzzy Entropy for Improved Software Maintenance.
Authors: Hooshyar, Maryam1 (AUTHOR), Izadkhah, Habib1 (AUTHOR) izadkhah@tabrizu.ac.ir, Karimpour, Jaber1 (AUTHOR)
Source: Journal of Software: Evolution & Process. May2026, Vol. 38 Issue 5, p1-26. 26p.
Subjects: Software maintenance, Software refactoring, Software measurement, Object-oriented programming, Empirical research, Computer software quality control
Abstract: Class cohesion is an important design aspect in object‐oriented software systems, impacting their development and maintenance. Existing literature proposes various object‐oriented cohesion metrics, aiming to measure the relationship among class members. However, these metrics differ in their approaches to measuring cohesion through interactions between attributes and methods. Anomalies such as lack of discrimination and sensitivity pose fundamental problems in cohesion metrics. The objective is to address the limitations of existing metrics and provide a more effective measure to compute the class cohesion. This paper introduces a new approach, SCBHFE, based on hesitant fuzzy entropy, to measure class cohesion. This approach not only uses shared attributes but also unshared attributes and neighbors of the method to evaluate class cohesion. The proposed method involves establishing a fuzzy set that corresponds to the target class and assigns membership degrees to individual methods. The SCBHFE metric is designed to preserve the four important properties of class cohesion theory. We empirically validate SCBHFE through a multifaceted evaluation. First, on a benchmark of 20 diverse classes, SCBHFE achieves 100% discrimination accuracy, successfully differentiating all key cohesion cases where 22 existing metrics, including LCOM1, LCOM2, LCOM3, LCOM5, Coh, CC, SCOM, LSCC, and LCC‐fail. Subsequently, applying SCBHFE to three large Java open‐source systems (Tomcat, Ant, and JFreeChart) confirms its practical effectiveness, consistently identifying low‐cohesion classes with values as low as 0.03–0.13. Statistical validation via principal component analysis further shows that SCBHFE captures a unique dimension of cohesion not addressed by prior metrics. Finally, SCBHFE demonstrates strong utility in detecting god classes in GanttProject and Xerces, thereby providing a reliable indicator for refactoring. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Software: Evolution & Process 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.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 194054111
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PubTypeId: academicJournal
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  Label: Title
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  Data: Quantifying Class Cohesion in Object‐Oriented Software Systems Using Hesitant Fuzzy Entropy for Improved Software Maintenance.
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  Data: <searchLink fieldCode="AR" term="%22Hooshyar%2C+Maryam%22">Hooshyar, Maryam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Izadkhah%2C+Habib%22">Izadkhah, Habib</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> izadkhah@tabrizu.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Karimpour%2C+Jaber%22">Karimpour, Jaber</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Software%3A+Evolution+%26+Process%22">Journal of Software: Evolution & Process</searchLink>. May2026, Vol. 38 Issue 5, p1-26. 26p.
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  Data: <searchLink fieldCode="DE" term="%22Software+maintenance%22">Software maintenance</searchLink><br /><searchLink fieldCode="DE" term="%22Software+refactoring%22">Software refactoring</searchLink><br /><searchLink fieldCode="DE" term="%22Software+measurement%22">Software measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Object-oriented+programming%22">Object-oriented programming</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+quality+control%22">Computer software quality control</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Class cohesion is an important design aspect in object‐oriented software systems, impacting their development and maintenance. Existing literature proposes various object‐oriented cohesion metrics, aiming to measure the relationship among class members. However, these metrics differ in their approaches to measuring cohesion through interactions between attributes and methods. Anomalies such as lack of discrimination and sensitivity pose fundamental problems in cohesion metrics. The objective is to address the limitations of existing metrics and provide a more effective measure to compute the class cohesion. This paper introduces a new approach, SCBHFE, based on hesitant fuzzy entropy, to measure class cohesion. This approach not only uses shared attributes but also unshared attributes and neighbors of the method to evaluate class cohesion. The proposed method involves establishing a fuzzy set that corresponds to the target class and assigns membership degrees to individual methods. The SCBHFE metric is designed to preserve the four important properties of class cohesion theory. We empirically validate SCBHFE through a multifaceted evaluation. First, on a benchmark of 20 diverse classes, SCBHFE achieves 100% discrimination accuracy, successfully differentiating all key cohesion cases where 22 existing metrics, including LCOM1, LCOM2, LCOM3, LCOM5, Coh, CC, SCOM, LSCC, and LCC‐fail. Subsequently, applying SCBHFE to three large Java open‐source systems (Tomcat, Ant, and JFreeChart) confirms its practical effectiveness, consistently identifying low‐cohesion classes with values as low as 0.03–0.13. Statistical validation via principal component analysis further shows that SCBHFE captures a unique dimension of cohesion not addressed by prior metrics. Finally, SCBHFE demonstrates strong utility in detecting god classes in GanttProject and Xerces, thereby providing a reliable indicator for refactoring. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Software: Evolution & Process 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: 10.1002/smr.70101
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        Text: English
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        PageCount: 26
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        Type: general
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        Type: general
      – SubjectFull: Software measurement
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      – SubjectFull: Object-oriented programming
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      – SubjectFull: Empirical research
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      – SubjectFull: Computer software quality control
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      – TitleFull: Quantifying Class Cohesion in Object‐Oriented Software Systems Using Hesitant Fuzzy Entropy for Improved Software Maintenance.
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            NameFull: Hooshyar, Maryam
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            NameFull: Izadkhah, Habib
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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