Guided, stochastic model-based GUI testing of Android apps

Su, Ting; Meng, Guozhu; Chen, Yuting; Wu, Ke; Yang, Weiming; Yao, Yao; Pu, Geguang; Liu, Yang; Su, Zhendong

doi:10.1145/3106237.3106298

Cited by 263 publications

(190 citation statements)

References 46 publications

Supporting

Mentioning

189

Contrasting

Unclassified

Order By: Relevance

“…Dynodroid [14] extends the random selection using weights and frequencies of events. Model-based strategies such as PUMA [8], DroidBot [11], MobiGUITAR [2], and Stoat [29] apply model-based testing to apps. Systematic exploration strategies range from full-scale symbolic execution [18] to evolutionary algorithms [15,17].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Does Diversity Improve the Test Suite Generation for Mobile Applications?

Vogel

Tran

Grunske

2019

Search-Based Software Engineering

View full text Add to dashboard Cite

In search-based software engineering we often use popular heuristics with default configurations, which typically lead to suboptimal results, or we perform experiments to identify configurations on a trialand-error basis, which may lead to better results for a specific problem. To obtain better results while avoiding trial-and-error experiments, a fitness landscape analysis is helpful in understanding the search problem, and making an informed decision about the heuristics. In this paper, we investigate the search problem of test suite generation for mobile applications (apps) using Sapienz whose heuristic is a default NSGA-II. We analyze the fitness landscape of Sapienz with respect to genotypic diversity and use the gained insights to adapt the heuristic of Sapienz. These adaptations result in Sapienz div that aims for preserving the diversity of test suites during the search. To evaluate Sapienz div , we perform a head-to-head comparison with Sapienz on 76 open-source apps.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Systematic exploration strategies range from full-scale symbolic execution [18] to evolutionary algorithms [15,17]. All of these approaches do not explicitly manage diversity, except of Stoat [29] encoding diversity of sequences into the objective function. Diversity in SBST.…”

Section: Related Workmentioning

confidence: 99%

Does Diversity Improve the Test Suite Generation for Mobile Applications?

Vogel

Tran

Grunske

2019

Search-Based Software Engineering

View full text Add to dashboard Cite

show abstract

“…STORYDROID extracts nearly 2 times more activity transitions than the stateof-the-art ATG extraction tool (i.e., IC3 [62]) on both opensource apps and closed-source apps. Besides, STORYDROID significantly outperforms the state-of-the-art dynamic testing tool (i.e., STOAT [67]) on activity coverage for both opensource apps (87% on average) and closed-source apps (74% on average). On average, our rendered images achieve 84% similarity compared with the ones that are dynamically obtained by STOAT.…”

Section: Introductionmentioning

confidence: 99%

“…First, ATGs are usually incomplete due to the limitation of static analysis tools [36,62]. Second, to identify all UI pages, a pure static approach may miss parts of UIs that are dynamically rendered (see Section III), whereas a pure dynamic approach [17,40,41,66,67] may not be able to reach all pages in the app, especially those requiring login. Third, the obfuscated activity names lack the semantics of corresponding functionalities, making the storyboard hard to understand.…”

Section: Introductionmentioning

confidence: 99%

StoryDroid: Automated Generation of Storyboard for Android Apps

Chen

Fan

Chen

et al. 2019

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

Self Cite

View full text Add to dashboard Cite

Mobile apps are now ubiquitous. Before developing a new app, the development team usually endeavors painstaking efforts to review many existing apps with similar purposes. The review process is crucial in the sense that it reduces market risks and provides inspiration for app development. However, manual exploration of hundreds of existing apps by different roles (e.g., product manager, UI/UX designer, developer) in a development team can be ineffective. For example, it is difficult to completely explore all the functionalities of the app in a short period of time. Inspired by the conception of storyboard in movie production, we propose a system, StoryDroid, to automatically generate the storyboard for Android apps, and assist different roles to review apps efficiently. Specifically, StoryDroid extracts the activity transition graph and leverages static analysis techniques to render UI pages to visualize the storyboard with the rendered pages. The mapping relations between UI pages and the corresponding implementation code (e.g., layout code, activity code, and method hierarchy) are also provided to users. Our comprehensive experiments unveil that StoryDroid is effective and indeed useful to assist app development. The outputs of StoryDroid enable several potential applications, such as the recommendation of UI design and layout code.

show abstract

“…Thus, to maximize app market success developers aim at attaining high quality software by revealing and fixing potential software bugs as early as possible [14]. As a natural consequence, in last years both researchers and pratictioners developed techniques and tools to automate the testing of mobile applications [8], [12], [13], [21]. Such tools aim to reveal unhandled exceptions while exercising the app under test (AUT) with input and system events.…”

Section: Introductionmentioning

confidence: 99%

BECLoMA: Augmenting stack traces with user review information

Pelloni

Grano

Ciurumelea

et al. 2018

2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)

View full text Add to dashboard Cite

Mobile devices such as smartphones, tablets and wearables are changing the way we do things, radically modifying our approach to technology. To sustain the brutal competition in the mobile market, developers need to deliver high quality applications in a short release cycle. Therefore, to maximize their market success, they aim to reveal and fix bugs as soon as possible. For this reason, researchers and practitioners proposed testing tools to automate the process of bug discovery and fixing. In the mobile development context, the content of user reviews represents an unmatched source for developers seeking for defects in their applications. However, no prior work explored the adoption of information available in user reviews for testing purposes. In this demo we present BECLoMA, a tool to enable the integration of user feedback in the testing process of mobile apps. BECLoMA links information from testing tools and user reviews, presenting developers an augmented testing report combining stack traces with user reviews information referring to the same crash. We show that BECLoMA facilitates not only the diagnosis and fix of app bugs, but also presents additional benefits: it eases the usage of testing tools and automates the analysis of user reviews from the Google Play Store. Abstract-Mobile devices such as smartphones, tablets and wearables are changing the way we do things, radically modifying our approach to technology. To sustain the high competition characterizing the mobile market, developers need to deliver high quality applications in a short release cycle. To reveal and fix bugs as soon as possible, researchers and practitioners proposed tools to automate the testing process. However, such tools generate a high number of redundant inputs, lacking of contextual information and generating reports difficult to analyze. In this context, the content of user reviews represents an unmatched source for developers seeking for defects in their applications. However, no prior work explored the adoption of information available in user reviews for testing purposes. In this demo we present BECLOMA, a tool to enable the integration of user feedback in the testing process of mobile apps. BECLOMA links information from testing tools and user reviews, presenting to developers an augmented testing report combining stack traces with user reviews information referring to the same crash. We show that BECLOMA facilitates not only the diagnosis and fix of app bugs, but also presents additional benefits: it eases the usage of testing tools and automates the analysis of user reviews from the Google Play Store.

show abstract

Guided, stochastic model-based GUI testing of Android apps

Cited by 263 publications

References 46 publications

Does Diversity Improve the Test Suite Generation for Mobile Applications?

Does Diversity Improve the Test Suite Generation for Mobile Applications?

StoryDroid: Automated Generation of Storyboard for Android Apps

BECLoMA: Augmenting stack traces with user review information

Contact Info

Product

Resources

About