Subsumption in Mutation Testing: An Automated Model Based on Genetic Algorithm

Tenório, Maria Cristina; Lopes, Roberta Vilhena Vieira; Fechine, Joseana Macêdo; Marinho, Tarsis; Costa, Evandro de Barros

doi:10.1007/978-3-030-14070-0_24

Cited by 4 publications

(1 citation statement)

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“…The concolic testing [35] is also applied to Simulink model to ensure the model safety. To generate inputs to find defects or wrong output signal patterns, different genetic algorithms [22,36] have been proposed. Those algorithms use different fitness functions and fitness definitions to guide the search.…”

Section: Related Workmentioning

confidence: 99%

Improve Model Testing by Integrating Bounded Model Checking and Coverage Guided Fuzzing

Yang

2023

Electronics

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Eectromechanical systems built by Simulink or Ptolemy have been widely used in industry fields, such as autonomous systems and robotics. It is an urgent need to ensure the safety and security of those systems. Test case generation technologies are widely used to ensure the safety and security. State-of-the-art testing tools employ model-checking techniques or search-based methods to generate test cases. Traditional search-based techniques based on Simulink simulation are plagued by problems such as low speed and high overhead. Traditional model-checking techniques such as symbolic execution have limited performance when dealing with nonlinear elements and complex loops. Recently, coverage guided fuzzing technologies are known to be effective for test case generation, due to their high efficiency and impressive effects over complex branches of loops. In this paper, we apply fuzzing methods to improve model testing and demonstrate the effectiveness. The fuzzing methods aim to cover more program branches by mutating valuable seeds. Inspired by this feature, we propose a novel integration technology SPsCGF, which leverages bounded model checking for symbolic execution to generate test cases as initial seeds and then conduct fuzzing based upon these worthy seeds. Over the evaluated benchmarks which consist of industrial cases, SPsCGF could achieve 8% to 38% higher model coverage and 3x-10x time efficiency compared with the state-of-the-art works.

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Section: Related Workmentioning

confidence: 99%