Model Based Statistical Testing and Durations

Böhr, Frank

doi:10.1109/ecbs.2010.49

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…In total, 79 test cases are generated. 16,11, and 12 of these test cases relate to Youtube, MB, and DLNA, respectively. In addition, there are 5 test cases for each of the remaining 8 features.…”

Section: Model Refinement and Resultsmentioning

confidence: 99%

“…In general, MBT has been utilized for statistical usage testing [11], [12], in which SUT models define the usage of different features of the system. Hereby, the probability/frequency of the utilization of different features are usually determined based on a usage profile [3], [13].…”

Section: Related Workmentioning

confidence: 99%

“…Recent studies of Bohr [11], [14] take the timing behavior and concurrency into account for model-based statistical testing of embedded systems. Petri nets are used for specifying SUT models [14] and oracles are automatically generated to enforce timing requirements [11].…”

Section: Related Workmentioning

confidence: 99%

“…Petri nets are used for specifying SUT models [14] and oracles are automatically generated to enforce timing requirements [11]. The generated test cases reflect the timed and concurrent nature of inputs for SUT.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Combining model-based and risk-based testing for effective test case generation

Gebizli¹,

Metin²,

Sözer

2015

2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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Due to copyright restrictions, the access to the full text of this article is only available via subscription.Model-based testing employs models of the system under test to automatically generate test cases. In this paper, we propose an iterative approach, in which these models are refined based on the principles of risk-based testing. We use Markov Chains as system models, in which transitions among system states are annotated with probabilities. Initially, these probability values are equal and as such, states have equal chances for being visited by the generated test cases. Memory leaks are monitored during the execution of these test cases. Then, transition probabilities are updated based on the risk that a failure can occur due to the observed memory leaks. We applied our approach in the context of an industrial case study for model-based testing of a Smart TV system. We observed promising results, in which several crash failures were detected after an iteration of model refinement. We aim at automating the whole process based on an adaptation model using the history of recorded memory leaks during previous test executions

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Section: Model Refinement and Resultsmentioning

confidence: 99%