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A regression test suite (RTS) is constructed to ensure that the changed parts of the system under test (SUT) behave as desired and that the unchanged parts of the SUT are not adversely affected. Model-based testing is a system testing technique in which systems are modeled by formal description languages, e.g., Extended Finite State Machine (EFSM) models. In this paper, a model-based RTS generation method based on EFSM dependence analysis is proposed. Twelve types of dependences are identified related to three types of elementary modifications (EMs), i.e., adding, deleting, and changing transitions in an EFSM model representing an SUT. These dependences capture the effects of the model on the EMs, the effects of the EMs on the model, and the side-effects of the EMs. The proposed method constructs an RTS by covering all occurrences of these dependences caused in a given EFSM model by a given set of EMs.

A trend in s o w a r e development is to assemble a system from a number of components. These may be either available commercially off-the-sheK or by the use of network-based resources. In many cases, the system is expected to function f o r multiple configurations of interchangeable components. The trade off that a system tester faces is the thoroughness of test component configuration coverage, versus limited resources of time and expense. This paper presents a metric that can be used to measure component interaction coverage of a set of system test configurations. We also provide a formal dejnition of the system test interaction problem.

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A model-based regression test suite (RTS) reduction method based on Extended Finite State Machine (EFSM) dependence analysis is proposed. Given an EFSM representing the requirements of a system under test (SUT) and a set of elementary modifications (EMs) on the EFSM, interaction patterns are identified related to each type of EMs, i.e., adding, deleting, and changing transitions in the EFSM. These interaction patterns capture the effects of the model on the EMs, the effects of the EMs on the model, and the side-effects of the EMs. The proposed method reduces the size of a given RTS by examining interaction patterns covered by each test case in the given RTS.