Generating transition probabilities to support model-based software testing

Walton, Gwendolyn H.; Poore, Jesse H.

doi:10.1002/1097-024x(200008)30:10<1095::aid-spe328>3.0.co;2-h

Cited by 32 publications

(15 citation statements)

References 8 publications

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“…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]. These values are reflected to the focus areas of the generated test cases.…”

Section: Related Workmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

“…We define a system model with Markov Chains, in which transitions among system states are annotated with probability values. These values are usually assigned based on functional importance or usage profile [3]. In our approach, they are initially equal to each other.…”

Section: Introductionmentioning

confidence: 99%

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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“…In developing a software model to be used in testing, it is important that it be reusable and flexible [19]. The model described here can be used for an application in any domain, given information about sequences of events for that application, rendering our model very flexible and applicable to any GUI application in the aforementioned subclass.…”

Section: Probabilistic Model Of Gui Interactionsmentioning

confidence: 99%

Automated gui testing guided by usage profiles

Brooks

Memon

2007

Proceedings of the Twenty-Second IEEE/ACM International Conference on Automated Software Engineering

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Most software developed in recent years has a graphical user interface (GUI). The only way for the end-user to interact with the software application is through the GUI. Hence, acceptance and system testing of the software requires GUI testing. This paper presents a new technique for testing of GUI applications. Information on the actual usage of the application, in the form of "usage profiles," is used to ensure that a new version of the application will function correctly. Usage profiles, sequences of events that end-users execute on a GUI, are used to develop a probabilistic usage model of the application. An algorithm uses the model to generate test cases that represent events the user is most likely to execute. Reverse engineering methods are used to extract the underlying structure of the application. An empirical study on four open source GUI applications reveals that test suites generated from the probabilistic model are 0.2-22% of the size of test suites produced directly from usage profiles. Furthermore, the test suites generated from the model detect more faults per test case than those detected directly from the usage profiles, and detect faults not detected by the original profiles.

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“…Usage models are often generated manually, possibly supported by dedicated languages [6] and tools [7], but can also be generated using systematic methods [8], [9], or mined from different artifacts such as log files [10].…”

Section: Introductionmentioning

confidence: 99%

Exploiting Common Object Usage in Test Case Generation

Zeller

2011

2011 Fourth IEEE International Conference on Software Testing, Verification and Validation

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Abstract-Generated test cases are good at systematically exploring paths and conditions in software. However, generated test cases often do not make sense. We adapt test case generation to follow patterns of common object usage, as mined from code examples. Our experiments show that generated tests thus (a) reuse familiar usage patterns, making them easier to understand and (b) focus on common usage, thus respecting implicit preconditions and avoiding meaningless tests.

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Generating transition probabilities to support model-based software testing

Cited by 32 publications

References 8 publications

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

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

Automated gui testing guided by usage profiles

Exploiting Common Object Usage in Test Case Generation

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