Object-oriented Cohesion Metrics Research Articles

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

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.

Measuring the Discriminative Power of Object-Oriented Class Cohesion Metrics

Several object-oriented cohesion metrics have been proposed in the literature. These metrics aim to measure the relationship between class members, namely, methods and attributes. Different metrics use different models to represent the connectivity pattern of cohesive interactions (CPCI) between class members. Most of these metrics are normalized to allow for easy comparison of the cohesion of different classes. However, in some cases, these metrics obtain the same cohesion values for different classes that have the same number of methods and attributes but different CPCIs. This leads to incorrectly considering the classes to be the same in terms of cohesion, even though their CPCIs clearly indicate that the degrees of cohesion are different. We refer to this as a lack of discrimination anomaly (LDA) problem. In this paper, we list and discuss cases in which the LDA problem exists, as expressed through the use of 16 cohesion metrics. In addition, we empirically study the frequent occurrence of the LDA problem when the considered metrics are applied to classes in five open source Java systems. Finally, we propose a metric and a simulation-based methodology to measure the discriminative power of cohesion metrics. The discrimination metric measures the probability that a cohesion metric will produce distinct cohesion values for classes with the same number of attributes and methods but different CPCIs. A highly discriminating cohesion metric is more desirable because it exhibits a lower chance of incorrectly considering classes to be cohesively equal when they have different CPCIs.

Dynamic cohesion measures for object-oriented software

Most of the object-oriented cohesion metrics proposed in the literature define static cohesion at class level. Measurement of object-level dynamic cohesion however gives better insight into the behavioural aspects of the system. In this paper, dynamic cohesion metrics are introduced which provide scope of cohesion measurement up to object level and take into account important and widely used object-oriented features such as inheritance, polymorphism and dynamic binding during measurement. A theoretical framework is introduced before defining the measures and a theoretic validation of the proposed measures is carried out to make them more meaningful. A dynamic analyser tool is developed using aspect-oriented programming (AOP) to perform dynamic analysis of Java applications for the purpose of collecting run-time data for computation of the proposed dynamic cohesion measures. Further, an experiment is carried out for the proposed dynamic cohesion metrics using 20 Java programs and this study shows that the proposed dynamic cohesion metrics are more accurate and useful in comparison to the existing cohesion metrics. Moreover, the proposed dynamic cohesion metrics are validated empirically using source code APIs of Java Development Kit (JDK) and the proposed metrics are found to be better indicators of change-proneness of classes than the existing cohesion metrics.

The interpretation and utility of three cohesion metrics for object-oriented design

The concept of cohesion in a class has been the subject of various recent empirical studies and has been measured using many different metrics. In the structured programming paradigm, the software engineering community has adopted an informal yet meaningful and understandable definition of cohesion based on the work of Yourdon and Constantine. The object-oriented (OO) paradigm has formalised various cohesion measures, but the argument over the most meaningful of those metrics continues to be debated. Yet achieving highly cohesive software is fundamental to its comprehension and thus its maintainability. In this article we subject two object-oriented cohesion metrics, CAMC and NHD, to a rigorous mathematical analysis in order to better understand and interpret them. This analysis enables us to offer substantial arguments for preferring the NHD metric to CAMC as a measure of cohesion. Furthermore, we provide a complete understanding of the behaviour of these metrics, enabling us to attach a meaning to the values calculated by the CAMC and NHD metrics. In addition, we introduce a variant of the NHD metric and demonstrate that it has several advantages over CAMC and NHD. While it may be true that a generally accepted formal and informal definition of cohesion continues to elude the OO software engineering community, there seems considerable value in being able to compare, contrast, and interpret metrics which attempt to measure the same features of software.

A comparison of cohesion metrics for object-oriented systems

Cohesion is the degree to which the elements of a class or object belong together. Many different object-oriented cohesion metrics have been developed; many of them are based on the notion of degree of similarity of methods. No consensus has yet arisen as to which of these metrics best measures cohesion; this is a problem for software developers since there are so many suggested metrics, it is difficult to make an informed choice. This research compares various cohesion metrics with ratings of two separate teams of experts over two software packages, to determine which of these metrics best match human-oriented views of cohesion. Additionally, the metrics are compared statistically, to determine which tend to measure the same kinds of cohesion. Differences in results for different object-oriented metrics tools are discussed.