Relationship Between Cohesion and Coupling Metrics for Object-Oriented Systems

Miquirice, Samuel António; Wazlawick, Raul Sidnei

doi:10.1007/978-3-319-99972-2_35

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…In another work, empirical study to investigate the interactions between various cohesion and coupling metrics in object-oriented system has been presented. It was observed that the relationship between cohesion and coupling is inversely associated with each other [36].…”

Section: Essential Quality Metrics For Microservice Based Systemmentioning

confidence: 99%

A Survey on Trending Topics of Microservices

2021

IJETER

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Microservice is an architectural style and a software development methodology. Microservices are used as a group of independent units with narrowly specified responsibilities that interact through well-described REST APIs. It is observed that the most challenging part in developing any application using microservice architecture is decomposing it into the correct level of granularity at design and run time, which requires good skills and domain knowledge. In this paper, some prominent topics in microservices analysis, such as determining the size and boundaries of microservices using various decomposition approaches and extraction of microservices from large monolithic applications have been discussed and analysed. Work pertaining to essential quality metrics required for a microservices-based system has also been surveyed. In this survey paper, we have identified how these topics are correlated and proposed some steps that might be beneficial in the transformation of monolithic applications into microservices.

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Section: Essential Quality Metrics For Microservice Based Systemmentioning

confidence: 99%

A Survey on Trending Topics of Microservices

2021

IJETER

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“…Nonetheless, the central importance of these metrics for software engineering encouraged researchers to propose even more coupling measures in an attempt to evaluate further connections between software entities. Therefore, research community is quite active in the derivation of new coupling metrics ( Myers, 1975 ; Henry, 1979 ; Henry & Kafura, 1981 ; Chidamber & Kemerer, 1991 ; Offutt, Harrold & Kolte, 1993 ; Dhama, 1995 ; Binkley & Schach, 1997a ; Harrison, Counsell & Nithi, 1998 ; Shao & Wang, 2003 ; Nachiappan & Thirumalesh, 2007 ; Miquirice & Wazlawick, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Vovel metrics—novel coupling metrics for improved software fault prediction

Rizwan

Nadeem

Sindhu

2021

PeerJ Computer Science

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Software is a complex entity, and its development needs careful planning and a high amount of time and cost. To assess quality of program, software measures are very helpful. Amongst the existing measures, coupling is an important design measure, which computes the degree of interdependence among the entities of a software system. Higher coupling leads to cognitive complexity and thus a higher probability occurrence of faults. Well in time prediction of fault-prone modules assists in saving time and cost of testing. This paper aims to capture important aspects of coupling and then assess the effectiveness of these aspects in determining fault-prone entities in the software system. We propose two coupling metrics, i.e., Vovel-in and Vovel-out, that capture the level of coupling and the volume of information flow. We empirically evaluate the effectiveness of the Vovel metrics in determining the fault-prone classes using five projects, i.e., Eclipse JDT, Equinox framework, Apache Lucene, Mylyn, and Eclipse PDE UI. Model building is done using univariate logistic regression and later Spearman correlation coefficient is computed with the existing coupling metrics to assess the coverage of unique information. Finally, the least correlated metrics are used for building multivariate logistic regression with and without the use of Vovel metrics, to assess the effectiveness of Vovel metrics. The results show the proposed metrics significantly improve the predicting of fault prone classes. Moreover, the proposed metrics cover a significant amount of unique information which is not covered by the existing well-known coupling metrics, i.e., CBO, RFC, Fan-in, and Fan-out. This paper, empirically evaluates the impact of coupling metrics, and more specifically the importance of level and volume of coupling in software fault prediction. The results advocate the prudent addition of proposed metrics due to their unique information coverage and significant predictive ability.

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2024

Object-Oriented Analysis and Design for Information Systems

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Relationship Between Cohesion and Coupling Metrics for Object-Oriented Systems

Cited by 5 publications

References 21 publications

A Survey on Trending Topics of Microservices

A Survey on Trending Topics of Microservices

Vovel metrics—novel coupling metrics for improved software fault prediction

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