ComboDroid

Wang, Jue; Jiang, Yanyan; Xu, Chang; Cao, Chun; Ma, Xiaoxing; Lü, Jian

doi:10.1145/3377811.3380382

Cited by 48 publications

(6 citation statements)

References 55 publications

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“…𝜔Test retrieves the covered properties by reading the files that record coverage information dumped by the instrumented AUT (Section 3.5) through Android's adb debugging tool [4]. Following previous work [34,48,56], we set a 200 ms delay between events. To switch to other fragmentstates (line 14 in Algorithm 1), 𝜔Test remembers the events that can lead to the switching of fragment-states during testing.…”

Section: Methodsmentioning

confidence: 99%

“…Whether two UI states are similar or different is determined by a trained neural network. • ComboDroid: ComboDroid [56] is a general-purpose Android test generation technique. Its core idea is to generate a long event sequence (a combo) by combining multiple short event sequences (use cases).…”

Section: Baselinesmentioning

confidence: 99%

“…Intuitively, more discovered GUI states that "look different" means more app behaviors are explored. Another major category looks for program properties (e.g., program statements and branches) in an app's program structure and takes them as the test objectives [45,46,51,56]. Although these existing program properties may be suitable for general-purpose testing, none of them are designed for testing WebViews in Android apps.…”

Section: Android Testingmentioning

confidence: 99%

“…For example, opening two different websites in a WebView-wrapped browser app may exercise the same page-loading process of We-bViews. Another typical category of conventional techniques is to guide test generation by the coverage of some program properties [45,46,51,56] that reflect the test objectives. Program statements and branches are two commonly adopted properties.…”

Section: Introductionmentioning

confidence: 99%

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ωTest: WebView-Oriented Testing for Android Applications

Wei

Liu

et al. 2023

Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis

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WebView is a UI widget that helps integrate web applications into the native context of Android apps. It provides powerful mechanisms for bi-directional interactions between the native-end (Java) and the web-end (JavaScript) of an Android app. However, these interaction mechanisms are complicated and have induced various types of bugs. To mitigate the problem, various techniques have been proposed to detect WebView-induced bugs via dynamic analysis, which heavily relies on executing tests to explore WebView behaviors. Unfortunately, these techniques either require manual effort or adopt random test generation approaches, which are not able to effectively explore diverse WebView behaviors. In this paper, we study the problem of test generation for WebViews in Android apps. Effective test generation for WebViews requires identifying the essential program properties to be covered by the generated tests. To this end, we propose WebView-specific properties to characterize WebView behaviors, and devise a cross-language dynamic analysis method to identify these properties. We develop 𝜔Test, a test generation technique that searches for event sequences covering the identified WebView-specific properties. An evaluation on 74 real-world open-/closed-source Android apps shows that 𝜔Test can cover diverse WebView behaviors and detect WebViewinduced bugs effectively. 𝜔Test detected 36 previously-unknown bugs. From the 22 bugs that we have reported to the app developers, 13 bugs were confirmed, 9 of which were fixed. CCS CONCEPTS• Software and its engineering → Software testing and debugging.

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Section: Methodsmentioning

confidence: 99%

Section: Baselinesmentioning

confidence: 99%

Section: Android Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ωTest: WebView-Oriented Testing for Android Applications

Wei

Liu

et al. 2023

Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis

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“…In particular, [15], [27], [50], [47], [23] reconstruct the app model statically, followed by dynamic exploration. Other techniques use static analysis to discover dependencies between different application components, and use it during the dynamic exploration [15], [43], [25], [38], [23], [14]. Another guided exploration technique CAR [45] uses a static constraint analysis to keep the symbolic execution scalable and obviate the need for whole program symbolic execution.…”

Section: Related Workmentioning

confidence: 99%

Columbus: Android App Testing Through Systematic Callback Exploration

Bose¹,

Das²,

Vasan³

et al. 2023

Preprint

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With the continuous rise in the popularity of Android mobile devices, automated testing of apps has become more important than ever. Android apps are event-driven programs. Unfortunately, generating all possible types of events by interacting with an app's interface is challenging for an automated testing approach. Callback-driven testing eliminates the need for event generation by directly invoking app callbacks. However, existing callback-driven testing techniques assume prior knowledge of Android callbacks, and they rely on a human expert, who is familiar with the Android API, to write stub code that prepares callback arguments before invocation. Since the Android API is very large and keeps evolving, prior techniques could only support a small fraction of callbacks present in the Android framework.In this work, we introduce COLUMBUS, a callback-driven testing technique that employs two strategies to eliminate the need for human involvement: (i) it automatically identifies callbacks by simultaneously analyzing both the Android framework and the app under test; (ii) it uses a combination of under-constrained symbolic execution (primitive arguments), and type-guided dynamic heap introspection (object arguments) to generate valid and effective inputs. Lastly, COLUMBUS integrates two novel feedback mechanisms-data dependency and crash-guidanceduring testing to increase the likelihood of triggering crashes and maximizing coverage. In our evaluation, COLUMBUS outperforms state-of-the-art model-driven, checkpoint-based, and callbackdriven testing tools both in terms of crashes and coverage.

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