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

Journal of Software Engineering and Applications 2012年第7期5卷 449-458页

作者： Lazhar Sadaoui mourad badri Linda badri Software Engineering Research Laboratory Department of Mathematics and Computer Science University of Quebec Trois-Rivières Canada

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.

关键词： Object-Oriented Classes Cohesion Similarity Clustering Metrics and Empirical Evaluation

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Empirical Analysis of Object-Oriented Design Metrics for Predicting Unit Testing Effort of Classes

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Journal of Software Engineering and Applications 2012年第7期5卷 513-526页

作者： mourad badri Fadel Toure Software Engineering Research Laboratory Department of Mathematics and Computer Science University of Quebec Trois- Rivières Canada Software Engineering Research Laboratory Department of Mathematics and Computer Science University of Quebec Trois-Rivières Canada

In this paper, we investigate empirically the relationship between object-oriented design metrics and testability of classes. We address testability from the point of view of unit testing effort. We collected data from three open source Java software systems for which JUnit test cases exist. To capture the testing effort of classes, we used metrics to quantify the corresponding JUnit test cases. Classes were classified, according to the required unit testing effort, in two categories: high and low. In order to evaluate the relationship between object-oriented design metrics and unit testing effort of classes, we used logistic regression methods. We used the univariate logistic regression analysis to evaluate the individual effect of each metric on the unit testing effort of classes. The multivariate logistic regression analysis was used to explore the combined effect of the metrics. The performance of the prediction models was evaluated using Receiver Operating Characteristic analysis. The results indicate that: 1) complexity, size, cohesion and (to some extent) coupling were found significant predictors of the unit testing effort of classes and 2) multivariate regression models based on object-oriented design metrics are able to accurately predict the unit testing effort of classes.

关键词： Software Testability Unit Testing Testing Effort Metrics Relationship Prediction and Empirical Analysis

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