An approach for Model-Driven test generation

The test phase is one of the most important phases in software development. However, in practice, little research has been carried out in this field. Model-Driven Engineering is a new paradigm that can help to minimize test cases generation costs and can ensure quality of results. This paper presents the application of the MDE paradigm in the systematic, even automatic, generation of System Test Software.Ministerio de Educación y Ciencia TIN2007-67843-C06 03Ministerio de Educación y Ciencia TIN2007-30391-

Automated Testing: Requirements Propagation via Model Transformation in Embedded Software

Testing is the most common activity to validate software systems and plays a key role in the software development process. In general, the software testing phase takes around 40-70% of the effort, time and cost. This area has been well researched over a long period of time. Unfortunately, while many researchers have found methods of reducing time and cost during the testing process, there are still a number of important related issues such as generating test cases from UCM scenarios and validate them need to be researched. As a result, ensuring that an embedded software behaves correctly is non-trivial, especially when testing with limited resources and seeking compliance with safety-critical software standard. It thus becomes imperative to adopt an approach or methodology based on tools and best engineering practices to improve the testing process. This research addresses the problem of testing embedded software with limited resources by the following. First, a reverse-engineering technique is exercised on legacy software tests aims to discover feasible transformation from test layer to test requirement layer. The feasibility of transforming the legacy test cases into an abstract model is shown, along with a forward engineering process to regenerate the test cases in selected test language. Second, a new model-driven testing technique based on different granularity level (MDTGL) to generate test cases is introduced. The new approach uses models in order to manage the complexity of the system under test (SUT). Automatic model transformation is applied to automate test case development which is a tedious, error-prone, and recurrent software development task. Third, the model transformations that automated the development of test cases in the MDTGL methodology are validated in comparison with industrial testing process using embedded software specification. To enable the validation, a set of timed and functional requirement is introduced. Two case studies are run on an embedded system to generate test cases. The effectiveness of two testing approaches are determined and contrasted according to the generation of test cases and the correctness of the generated workflow. Compared to several techniques, our new approach generated useful and effective test cases with much less resources in terms of time and labor work. Finally, to enhance the applicability of MDTGL, the methodology is extended with the creation of a trace model that records traceability links among generated testing artifacts. The traceability links, often mandated by software development standards, enable the support for visualizing traceability, model-based coverage analysis and result evaluation

Supporting Change in Product Lines Within the Context of Use Case-driven Development and Testing

Product Line Engineering (PLE) is a crucial practice in many software development environments where systems are complex and developed for multiple customers with varying needs. At the same time, many business contexts are use case-driven where use cases are the main artifacts driving requirements elicitation and many other development activities. In these contexts, variability information is often not explicitly represented, which leads to ad-hoc change management for use cases, domain models and test cases in product families. In this thesis, we address the problems of modeling variability in requirements with additional traceability to feature models and the manual and error prone requirements configuration and regression testing in product families. We provide the following contributions: - A modeling method for capturing variability information in product line use case and domain models by relying exclusively on commonly used artifacts in use-case driven development, thus avoiding unnecessary modeling overhead. - An approach for automated configuration of product specific use case and domain models that guides customers in making configuration decisions and automatically generates use case diagrams, use case specifications, and domain models for configured products. - A change impact analysis approach for evolving configuration decisions in product line use case models that automatically identifies the impact of decision changes on other decisions, and incrementally reconfigures product specific use case diagrams and specifications for evolving decisions. - An approach for automated classification and prioritization of system test cases in a family of products that automatically classifies and prioritizes, for each new product, system test cases of previous product(s) in a product line, and provides guidance in modifying existing system test cases to cover new use case scenarios that have not been tested in the product line before. All our approaches have been developed and evaluated in close collaboration with our industry partner IEE