Widespread adoption of model-centric development has created opportunities for software testing, with Model-Based Testing (MBT). MBT supports the generation of test cases from models and the demonstration of model and source-code compliance. Models evolve, much like source code. Thus, an important activity of MBT is selective regression testing, which selects test cases for retest based on model modifications, rather than source-code modifications. This activity explores relationships between model elements and test cases that traverse those elements to locate retest able test cases. We contribute an approach and prototype to model-based selective regression testing, whereby fine-grain traceability relationships among entities in models and test cases are persisted into a traceability infrastructure throughout the test generation process: the relationships represent reasons for test case creation and are used to select test cases for re-run. The approach builds upon existing regression test selection techniques and adopts scenarios as behavioral modeling perspective. We analyze precision, efficiency and safety of the approach through case studies and through theoretical and intuitive reasoning.

Throughout their life cycle, software artifacts are modified, and selective regression testing is used to identify the negative impact of modifications. Code-based regression test selection retests test cases sub-set that traverse code modifications. It uses recovered relationships between code parts and test cases that traverse them to locate test cases for retest when code is modified. Broad adoption of model-centric development has created opportunities for software testing. It enabled driving testing processes at higher abstraction levels and demonstrating code to model compliance by means of Model-Based Testing (MBT). Models also evolve, so an important activity of MBT is selective regression testing. It selects test cases for retest based on model modification, so it relies on relationships between model elements and test cases that traverse those elements to locate test cases for retest. We contribute an approach and prototype that during test case generation creates fine-grained traceability relationships between model elements and test cases, which are used to support model-based regression test selection.

Model-driven development is leading to increased use of models in conjunction with source code in software testing. Model-based testing, however, introduces new challenges for testing activities, which include creation and maintenance of traceability information among test-related artifacts. Traceability is required to support activities such as selective regression testing. In fact, most model-based testing automated approaches often concentrate on the test generation and execution activities, while support to other activities is limited (e.g. model-based selective regression testing, coverage analysis and behavioral result evaluation)

The adoption of model-driven development is leading to increased use of models in conjunction with source code in software testing. Model-based testing, however, introduces new challenges for testing activities, which include creation and maintenance of traceability information among test-related artifacts. Traceability is required to support activities such as model-based result evaluation, regression testing and coverage analysis. In this paper, we present an automated approach that leverages model transformation techniques to support test generation. The test generation process includes creation of test-related models and fine-grained relationships among these models. We also motivate our approach with a simple example demonstrating support for model-based regression testing