Discovery of Real World Context Event Patterns for Smartphone Devices Using Conditional Random Fields

Piparia, Shraddha; Khan, Khorrom; Bryce, Renée

doi:10.1007/978-3-030-70416-2_29

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Each test case has a unique id, a set of events, and a length. To generate the CRF model, we observed everyday smartphone use from 58 university students for the duration of one month, via a monitoring app [1]. The app was configured to listen to 144 broadcasts which occurred 16,257,795 times during the one month period.…”

Section: Screen Orientationmentioning

confidence: 99%

“…The app was configured to listen to 144 broadcasts which occurred 16,257,795 times during the one month period. [1]. This data fed into the construction of CRFs using install AUT and execute launch event, e launch 8:…”

Section: Screen Orientationmentioning

confidence: 99%

“…Additional examples of context events include changes in battery levels, the device's physical location, sound output to speakers or headphones, and changes to screen orientation. The work in this paper generates context aware test suites with a strategy based on a real-world data set of context events [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data Driven Testing for Context Aware Apps

Michaels¹,

Piparia²,

Adamo³

et al. 2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

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Context driven environments are growing in popularity. Mobile applications, Internet of Things devices, autonomous vehicles, and future technologies respond to context events in their environments. This work uses a set of context events from real users to guide the generation of context driven test cases. Context event sequences are obtained by applying Conditional Random Fields (CRF). Test suites are then constructed by interleaving the context event sequences with GUI events. The choice of context event is made based on transitions obtained from the CRF. Results of the empirical studies show that techniques that incorporate context events provide better code coverage than NoContext for the subject applications. A heuristic technique introduced in this work, ISFreqOne, yields 4x better coverage than NoContext, 0.06x better coverage than Random Start Context, 0.05x better coverage than Iterative Start Context, which are control context generation techniques, and 0.04x better coverage than ISFreqTwo, another heuristic introduced in this work.

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Section: Screen Orientationmentioning

confidence: 99%

Section: Screen Orientationmentioning

confidence: 99%

See 1 more Smart Citation

Data Driven Testing for Context Aware Apps

Michaels¹,

Piparia²,

Adamo³

et al. 2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…These input methods and gestures need to be considered when developing test generation tools for Android applications. Dynamic UI management, system-generated events, inter-app communications, and context awareness[94] [45] are some of the other major challenges for Android test automation[100].2.2. Software TestingSoftware testing is a methodical and analytical procedure aimed at assessing a software system or application to ensure that it satisfies predetermined specifications, performs as designed, and functions consistently.…”

mentioning

confidence: 99%

Reinforcement Learning-Based Test Case Generation with Test Suite Prioritization for Android Application Testing

Khan

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This dissertation introduces a hybrid strategy for automated testing of Android applications that combines reinforcement learning and test suite prioritization. These approaches aim to improve the effectiveness of the testing process by employing reinforcement learning algorithms, namely Q-learning and SARSA (State-Action-Reward-State-Action), for automated test case generation. The studies provide compelling evidence that reinforcement learning techniques hold great potential in generating test cases that consistently achieve high code coverage; however, the generated test cases may not always be in the optimal order. In this study, novel test case prioritization methods are developed, leveraging pairwise event interactions coverage, application state coverage, and application activity coverage, so as to optimize the rates of code coverage specifically for SARSA-generated test cases. Additionally, test suite prioritization techniques are introduced based on UI element coverage, test case cost, and test case complexity to further enhance the ordering of SARSA-generated test cases. Empirical investigations demonstrate that applying the proposed test suite prioritization techniques to the test suites generated by the reinforcement learning algorithm SARSA improved the rates of code coverage over original orderings and random orderings of test cases.

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