Integrating Model-Based Testing with Evolutionary Functional Testing

Abstract

Evolutionary Functional Testing (EFT) is a relatively recent approach to automating the testing process. The research presented in this paper aims at increasing the acceptability of EFT in industrial settings. An approach suitable for efficiently and effectively testing complex continuous control systems is introduced. The main focus is on generating realistic test stimuli, enabling interactivity between test driver and test object, and facilitating the process of designing a suitable fitness function. This is accomplished by integrating EFT with model-based testing methodologies resulting in an intuitive testing approach that enables even testers not familiar with search based testing to achieve good results with limited effort. A test environment optimized for deployment in the industrial domain is introduced. Features of the test environment include the capability of automatically generating realistic continuous test data sets, interacting with the system under test during test execution, and automatically executing and evaluating large numbers of tests. A thorough case study using an adaptive cruise control system from the automotive domain is performed to assess the approach. Results of this work indicate high usability, efficiency, and effectiveness of the proposed method for testing complex embedded systems.