Revising cohesion measures by considering the impact of write interactions between class members

Woo, Gyun; Chae, Heung Seok; Cui, Jian Feng; Ji, Jeong-Hoon

doi:10.1016/j.infsof.2008.05.014

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…Metrics that indirectly quantify coupling and dependencies were also investigated, S. Black in [26] has investigated the ripple effects in non-OO software projects, he used four variations of McCabe's complexity metric to propose the Ripple Effect and Stability Tool (REST) which was based on an approximated algorithm to assess the SCIA for C programs. Researchers in [27] have investigated the correlation of the cohesion metrics with the impact of write interactions between class members and have proposed revisions to the existing cohesion metrics to consider the impact of the write interactions. Code metrics were also used as features with machine learning techniques to build prediction models of SCIA, Abdi et al in [28] proposed prediction models built using OO coupling metrics and several classification algorithms.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Software Co-Change Prediction: Leveraging Hybrid Approaches for Improved Accuracy

Zagane,

Alenezi

2024

IEEE Access

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Accurate prediction of co-changes in software systems is crucial for efficient development and maintenance, especially as systems grow in complexity. While deep learning-based approaches have shown promise, they often struggle with diverse and complex data. In this paper, we present a novel hybrid approach that combines traditional software engineering methods with deep learning techniques to improve co-change prediction accuracy. Our approach leverages software metrics and deep learning models, incorporating the unique characteristics induced by naming conventions, such as PascalCase and camelCase, used by developers for naming consistency. By utilizing char n-gram embedding and sub-token context, we enrich the vector representations of source file names, capturing relationships and dependencies between files. We comprehensively evaluate our hybrid approach using three open-source software projects. The findings of this study have significant implications for the development of more effective software co-change prediction tools and techniques, enabling better decision-making in software development and maintenance processes. Our approach outperforms traditional software engineering methods and deep learning-based approaches, demonstrating its potential to significantly improve software development and maintenance efficiency.

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Section: Related Workmentioning

confidence: 99%

Enhancing Software Co-Change Prediction: Leveraging Hybrid Approaches for Improved Accuracy

Zagane,

Alenezi

2024

IEEE Access

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“…Briand et al [34] developed a unified framework for cohesion measurement in OO systems that classifies and discusses several cohesion metrics. Development of metrics for class cohesion assessment still continues [3,[5][6][7][8][9][10][11][12][13][14]37].…”

Section: Object-oriented Cohesion Metricsmentioning

confidence: 99%

“…As a result, these metrics may not be completely reliable to be used to detect effectively weaknesses in the design (assignment of disparate roles to classes for example), or identify refactoring opportunities. The debate on cohesion metrics still continues and new definitions are proposed [3,[5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Improving Class Cohesion Measurement: Towards a Novel Approach Using Hierarchical Clustering

Sadaoui¹,

Badri²,

Badri

2012

JSEA

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Class cohesion is considered as one of the most important object-oriented software attributes. High cohesion is, in fact, a desirable property of software. Many different metrics have been suggested in the last several years to measure the cohesion of classes in object-oriented systems. The class of structural object-oriented cohesion metrics is the most in-vestigated category of cohesion metrics. These metrics measure cohesion on structural information extracted from the source code. Empirical studies noted that these metrics fail in many situations to properly reflect cohesion of classes. This paper aims at exploring the use of hierarchical clustering techniques to improve the measurement of cohesion of classes in object-oriented systems. The proposed approach has been evaluated using three particular case studies. We also used in our study three well-known structural cohesion metrics. The achieved results show that the new approach appears to better reflect the cohesion (and structure) of classes than traditional structural cohesion metrics

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“…Briand et al (Briand, 1998) developed a unified framework for cohesion measurement in OO systems that classifies and discusses several cohesion metrics. Development of metrics for class cohesion assessment still continues (Badri, 2008;Chae, 2004;Chen, 2002;Counsell, 2006;Marcus, 2005;Marcus, 2008;Meyers, 2004;Woo, 2009;Zhou, 2003). Recent approaches for assessing class cohesion focus on semantic cohesion (De Lucia, 2008;Marcus, 2008).…”

Section: Cohesion Metricsmentioning

confidence: 99%

Exploring Empirically the Relationship Between Lack of Cohesion in Object-Oriented Systems and Coupling and Size

Badri¹,

Badri²,

Touré³

2010

Proceedings of the 5th International Conference on Software and Data Technologies

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The study presented in this paper aims at exploring empirically the relationship between lack of cohesion of classes in object-oriented systems and their coupling and size. We designed and conducted an empirical study on various open source Java software systems. The experiment has been conducted using several well known code-based metrics related to cohesion, coupling and size. The results of this study provide evidence that a lack of cohesion may actually be associated with (high) coupling and (large) size.

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Revising cohesion measures by considering the impact of write interactions between class members

Cited by 7 publications

References 22 publications

Enhancing Software Co-Change Prediction: Leveraging Hybrid Approaches for Improved Accuracy

Enhancing Software Co-Change Prediction: Leveraging Hybrid Approaches for Improved Accuracy

Improving Class Cohesion Measurement: Towards a Novel Approach Using Hierarchical Clustering

Exploring Empirically the Relationship Between Lack of Cohesion in Object-Oriented Systems and Coupling and Size

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