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We present UMTG, a toolset for automatically generating executable and traceable system test cases from use case specifications. UMTG employs Natural Language Processing (NLP), a restricted form of use case specifications, and constraint solving. Use cases are expected to follow a template with restriction rules that reduce imprecision and enable NLP. NLP is used to capture the control flow implicitly described in use case specifications. Finally, to generate test input, constraint solving is applied to OCL constraints referring to the domain model of the system. UMTG is integrated with two tools that are widely adopted in industry, IBM Doors and Rhapsody. UMTG has been successfully evaluated on an industrial case study.

In safety critical domains, system test cases are often derived from functional requirements in natural language (NL) and traceability between requirements and their corresponding test cases is usually mandatory. The definition of test cases is therefore time-consuming and error prone, especially so given the quickly rising complexity of embedded systems in many critical domains. Though considerable research has been devoted to automatic generation of system test cases from NL requirements, most of the proposed approaches re- quire significant manual intervention or additional, complex behavioral modelling. This significantly hinders their applicability in practice. In this paper, we propose Use Case Modelling for System Tests Generation (UMTG), an approach that automatically generates executable system test cases from use case spec- ifications and a domain model, the latter including a class diagram and constraints. Our rationale and motivation are that, in many environments, including that of our industry partner in the reported case study, both use case specifica- tions and domain modelling are common and accepted prac- tice, whereas behavioural modelling is considered a difficult and expensive exercise if it is to be complete and precise. In order to extract behavioral information from use cases and enable test automation, UMTG employs Natural Language Processing (NLP), a restricted form of use case specifica- tions, and constraint solving.

In modern pervasive application domains, such as Service Oriented Architectures (SOAs) and Peer-to-Peer (P2P) systems, security aspects are critical. Justified confidence in the security mechanisms that are implemented for assuring proper data access is a key point. In the last years XACML has become the de facto standard for specifying policies for access control decisions in many application domains. Briefly, an XACML policy defines the constraints and conditions that a subject needs to comply with for accessing a resource and doing an action in a given environment. Due to the complexity of the language, XACML policy specification is a difficult and error prone process that requires specific knowledge and a high effort to be properly managed.

Porting an application written for personal computer to embedded devices requires conversion of floating-point numbers and operations into fixed-point ones. Testing the conversion hence requires the latter be as close as possible to the former. The closeness is orthogonal to code coverage and requires different strategies to generate a test suite that reveals the gap between the two functions. We introduce a new test adequacy criterion and propose several metrics to quantify the closeness of two functions. After that we propose a method to generate a better test suite from a given one for the test adequacy criteria. We also show experimental results on some well-known mathematical functions.