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Empirical validation of software metrics to predict quality using machine learning methods is important to ensure their practical relevance in the software organizations. In this paper, we build a Support Vector Machine (SVM) model to find the relationship between object-oriented metrics given by Chidamber and Kemerer and fault proneness. The proposed model is empirically evaluated using public domain KC1 NASA data set. The performance of the SVM method was evaluated by Receiver Operating Characteristic (ROC) analysis. Based on these results, it is reasonable to claim that such models could help for planning and performing testing by focusing resources on fault-prone parts of the design and code. Thus, the study shows that SVM method may also be used in constructing software quality models. [ABSTRACT FROM AUTHOR]

Use case refactoring is one of the recent software engineering techniques that aimed at synthesising and refine use case models. Two new types of use case refactoring are proposed in this paper. First, behavioural refactoring aimed at synthesising the presentation and understanding of the described services. Second, structural refactorings aimed at refining and simplifying the different relationship between the following pairs: (1) concrete and abstract use cases, and (2) use cases and actors. The application of the proposed refactorings on a real use case model showed that the advantages of use case refactorings are not limited to the target use case models only, but on their relationships with other software engineering artefacts. These include the facilitation of the extraction and utilisation of (1) use case patterns, (2) software metrics, and (3) software cost estimates from use case models. Further work is being carried out to automate the process of use case refactoring and integrate it with the underlying software development process. [ABSTRACT FROM AUTHOR]