Automatic test case generation from Simulink/Stateflow models using model checking

Abstract

SUMMARYModel‐based test generation techniques based on random input generation and guided simulation do not satisfy the demands of high test coverage and completeness guarantees as required by safety‐critical applications. Recently, test generation techniques based on model checking have been reported to bridge this gap. To evaluate the effectiveness of these techniques, an in‐house tool suite, AutoMOTGen, has been developed for Simulink/Stateflow and applied on real‐life case studies at General Motors. This paper outlines the test generation methodology of AutoMOTGen and gives a comparative study with a commercial, primarily random input‐based, test generation tool on the same set of examples. The results indicate that in terms of coverage, model checking‐based techniques complement the random input‐based techniques. In addition, they provide proofs for unreachability that can aid in debugging the models. Therefore, it is recommended that model checking‐based tools be utilized to complement and enhance the effectiveness of model‐based testing methods in safety‐critical systems engineering. Copyright © 2013 John Wiley & Sons, Ltd.

Automated test suite generation for time-continuous simulink models

“…A number of model-based testing techniques have been applied to Simulink models with the aim of achieving high structural coverage or detecting a large number of mutants. For example, search-based approaches [54,55], reachability analysis [33,21], guided random testing [43,44], and a combination of these techniques [50,39,42,36,20,8] have been previously applied to Simulink models to generate coverage-adequate test suites. Alternatively, various search-based [61,62] and bounded reachability analysis [9] techniques have been used to generate mutant-killing test suites from Simulink models.…”

Effective test suites for mixed discrete-continuous stateflow controllers

“…These model-based testing approaches often generate test cases from models using various automation mechanisms, e.g., search-based techniques [50], model checking [27,42], guided random testing [37,33,11] or a combination of these techniques [36,30]. In [31], a model-based testing approach for mixed discrete-continuous Stateflow models is proposed where test inputs are generated based on discrete fragments of Stateflow models, and are applied to the original models to obtain test oracles in terms of continuous signals.…”

Analysis and testing of matlab simulink models: a systematic mapping study

“…The remaining four articles [6,9,32,41] discuss a combination of testing and analysis. A tool for test case generation called SmartTestGen is introduced in [6].…”

“…This tool integrates different test generation techniques supported by analysis, e.g., random testing, constraint solving, model checking, and heuristics. In [9], generation of tests based on model checking is presented, and different test generation techniques are analyzed.…”