Dynamic testing via automata learning

Raffelt, Harald; Merten, Maik; Steffen, Bernhard; Margaria, Tiziana

doi:10.1007/s10009-009-0120-7

Cited by 74 publications

(38 citation statements)

References 25 publications

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“…Moreover, l should lead to a state q from the current state in the model. If such a l is not found, we know that our model is equivalent to the target model (lines [35][36]. On the other hand, if such an l exists, the algorithm executes the app on l and checks if the resulting trace is consistent with the model (lines [30][31].…”

Section: Learning Guided Testing Algorithmmentioning

confidence: 99%

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Guided GUI testing of android apps with minimal restart and approximate learning

ChoiWontae¹,

NeculaGeorge²,

SenKoushik³

2013

SIGPLAN Not.

168

127

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Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. Hundreds of thousands of specialized applications, called apps, are available for such mobile platforms. Manual testing is the most popular technique for testing graphical user interfaces of such apps. Manual testing is often tedious and error-prone. In this paper, we propose an automated technique, called SwiftHand, for generating sequences of test inputs for Android apps. The technique uses machine learning to learn a model of the app during testing, uses the learned model to generate user inputs that visit unexplored states of the app, and uses the execution of the app on the generated inputs to refine the model. A key feature of the testing algorithm is that it avoids restarting the app, which is a significantly more expensive operation than executing the app on a sequence of inputs. An important insight behind our testing algorithm is that we do not need to learn a precise model of an app, which is often computationally intensive, if our goal is to simply guide test execution into unexplored parts of the state space. We have implemented our testing algorithm in a publicly available tool for Android apps written in Java. Our experimental results show that we can achieve significantly better coverage than traditional random testing and L * -based testing in a given time budget. Our algorithm also reaches peak coverage faster than both random and L * -based testing.

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Section: Learning Guided Testing Algorithmmentioning

confidence: 99%

“…Testing with model learning [18,34,35] tries to address the limitations of modelbased testing by learning a model of the app as testing is performed. An active learning algorithm is used in conjunction with a testing engine to learn a model of the GUI app and to guide the generation of user input sequences based on the model.…”

mentioning

confidence: 99%

Guided GUI testing of android apps with minimal restart and approximate learning

ChoiWontae¹,

NeculaGeorge²,

SenKoushik³

2013

SIGPLAN Not.

168

127

View full text Add to dashboard Cite

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“…These experiments support the thesis that LBT can substantially outperform random testing as a black-box requirements testing method. Several previous works, (for example Peled et al [13], Groce et al [5] and Raffelt et al [14]) have also considered a combination of learning and model checking to achieve testing and/or formal verification of reactive systems. Within the model checking community, the verification approach known as counterexample guided abstraction refinement (CEGAR) also combines learning and model checking (see e.g.…”

Section: Related Workmentioning

confidence: 99%

Case Studies in Learning-Based Testing

Feng

Lundmark²,

Michaëlsson

et al. 2013

Testing Software and Systems

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Abstract. We present case studies which show how the paradigm of learning-based testing (LBT) can be successfully applied to black-box requirements testing of industrial reactive systems. For this, we apply a new testing tool LBTest, which combines algorithms for incremental black-box learning of Kripke structures with model checking technology. We show how test requirements can be modeled in propositional linear temporal logic extended by finite data types. We then provide benchmark performance results for LBTest applied to three industrial case studies.

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“…Several previous studies, (for example [19], [9] and [20]) have considered a combination of learning and model checking to achieve testing and/or formal verification of reactive systems. Within the model checking community the verification approach known as counterexample guided abstraction refinement (CE-GAR) also combines learning and model checking, (see e.g.…”

Section: Related Workmentioning

confidence: 99%