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A key activity in the introduction of service oriented architecture (SOA) for an organization is to evaluate the suitability of existing assets for service orientation. We identify the core principles of SOA as the guide lines in evaluating the suitability of the existing assets. The existing metrics and guidelines that could be helpful in evaluating these principles are surveyed. This would benefit an organization in understanding the effort needed for migration and also to build proper services from the existing assets.

We propose a novel black-box approach to reverse engineer the state model of software components. We assume that in different states, a component supports different subsets of its services and that the state of the component changes solely due to invocation of its services. To construct the state model of a component, we track the changes (if any) to its supported services that occur after invoking various services. Case studies carried out by us show that our approach generates state models with sufficient accuracy and completeness for components with services that either require no input data parameters or require parameters with small set of values.

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Regression test selection techniques for embedded programs have scarcely been reported in the literature. In this paper, we propose a model-based regression test selection technique for embedded programs. Our proposed model, in addition to capturing the data and control dependence aspects, also represents several additional program features that are important for regression test case selection of embedded programs. These features include control flow, exception handling, message paths, task priorities, state information and object relations. We select a regression test suite based on slicing our proposed graph model. We also propose a genetic algorithm-based technique to select an optimal subset of test cases from the set of regression test cases selected after slicing our proposed model.