Object-oriented analysis and design approach has become the mainstream of today¡¦s systems development technique. The Timing Diagram and State Machine Diagram from the Unified Modeling Language (UML) are becoming the major tools for modeling the platform independent model (PIM) of an embedded system. Once these two diagrams are constructed, the results can then be transformed into its platform specific model and template code based on the model driven architecture (MDA) approach. However, the detailed guideline for the transformation from Timing Diagram and State Machine Diagram to the template code is lacking. Therefore, this study presents a methodology which provides guidelines for the transformation from State Machine Diagram and Timing Diagram to template code for the embedded system. The research methodology is articulated using the design science research methodology. A usability evaluation with a real-world embedded system case is performed to demonstrate its applicability. The results indicated that with this proposed method, the system developer can effectively transform the PIM of an embedded system into its template code.

Yes / Automatic test data generation helps testers to validate software against user requirements more easily. Test data can be generated from many sources; for example, experience of testers, source program, or software specification. Selecting a proper test data set is a decision making task. Testers have to decide what test data that they should use, and a heuristic technique is needed to solve this problem automatically. In this paper, we propose a framework for generating test data from software specifications. The selected specification is Unified Modeling Language (UML) state machine diagram. UML state machine diagram describes a system in term of state which can be changed when there is an action occurring in the system. The generated test data is a sequence of these actions. These sequences of action help testers to know how they should test the system. The quality of generated test data is measured by the number of transitions which is fired using the test data. The more transitions test data can fire, the better quality of test data is. The number of coverage transitions is also used as a feedback for a heuristic search for a better test set. Genetic algorithms (GAs) are selected for searching the best test data. Our experimental results show that the proposed GA-based approach can work well for generating test data for some types of UML state machine diagrams.