To automate the refactoring identification process, a large number of candidates need to be compared. Such an overhead can make the refactoring approach impractical if the software size is large and the computational load of a fitness function is substantial. In this paper, we propose a two-phase assessment approach to improving the efficiency of the process. For each iteration of the refactoring process, refactoring candidates are preliminarily assessed using a lightweight, fast delta assessment method called the Delta Table. Using multiple Delta Tables, candidates to be evaluated with a fitness function are selected. A refactoring can be selected either interactively by the developer or automatically by choosing the best refactoring, and the refactorings are applied one after another in a stepwise fashion. The Delta Table is the key concept enabling a two-phase assessment approach because of its ability to quickly calculate the varying amounts of maintainability provided by each refactoring candidate. Our approach has been evaluated for three large-scale open-source projects. The results convincingly show that the proposed approach is efficient because it saves a considerable time while still achieving the same amount of fitness improvement as the approach examining all possible candidates.

Context-awareness of mobile applications yields several issues for testing, since the mobile applications should be testable in any environment and with any contextual input.In previous studies of testing for Android applications as eventdriven systems, many researchers have focused on using the generated test cases considering only GUI events.However, it is difficult to detect failures in the changes in the context in which applications run.It is important to consider various contexts since the mobile applications adapt and use novel features and sensors of mobile devices.In this paper, we provide the method of systematically generating various executing contexts from permissions.By referring the lists of permissions, the resources that the applications use for running Android applications can be inferred easily.The various contexts of an application can be generated by permuting resource conditions, and the permutations of the contexts are prioritized.We have evaluated the usefulness and effectiveness of our method by showing that our method contributes to detect faults.

For automating refactoring identification, previous methods for assessing the impact of a large number of refactoring candidates may be computationally expensive. In our paper, we propose an efficient method for assessing the impact of refactoring candidates on maintainability based on matrix computation, which is approximate but fast. This proposed method is evaluated on a refactoring identification approach for Edit and Columba, two large-scale open source projects. The experiments show that the proposed method requires less time for assessing refactoring candidates and that the refactoring identification approach using our proposed method also improves maintainability.

During the development and maintenance of object-oriented (OO) software, the information on the classes which are more prone to be changed is very useful. Developers and maintainers can make a more flexible software by modifying the part of classes which are sensitive to changes. Traditionally, most change-proneness prediction has been studied based on source codes. However, change-proneness prediction in the early phase of software development can provide an easier way for developing a stable software by modifying the current design or choosing alternative designs before implementation. To address this need, we present a systematic method for calculating the behavioral dependency measure (BDM) which helps to predict change-proneness in UML 2.0 models. The proposed measure has been evaluated on a multi-version medium size open-source project namely JFreeChart. The obtained results show that the BDM is an useful indicator and can be complementary to existing OO metrics for change-proneness prediction.