A/B test tools of native mobile application

Adinata, Muhammad; Liem, Inggriani

doi:10.1109/icodse.2014.7062683

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…A/B testing is recurrently used for statistical hypothesis tests. In [112], Adinata et al have applied A/B testing to test mobile apps, where they have solved three challenges of applying A/B testing, including element composition, variant delivery and internet connection. Holzmann et al [109] conduct A/B testing through a multivariate testing tool.…”

Section: Test Methodologiesmentioning

confidence: 99%

“…[43] SIG-Droid [99] Knorr et al [44] TAST [100] IntentDroid [45] Griebe et al [101] Farto et al [46] Bielik et al [47] MobiGUITAR [48] AGRippin [102] Aktouf et al [49] THOR [103] AppAudit [50] Morgado et al [104] Hassanshahi et al [51] iMPAcT [52] Deng et al [53] Espada et al [54] Zhang et al [55] QUANTUM [56] CRAXDroid [57] IntentFuzzer [58] Vikomir et al [59] Shahriar et al [60] APSET [61] DROIDRACER [62] EvoDroid [105] SPAG-C [106] Caiipa [107] UGA [108] AppACTS [63] CAFA [64] Holzmann et al [109] Guo et al [65] Griebe et al [66] PBGT [67] Chen et al [110] Banerjee et al [68] Amalfitano et al [111] Adinata et al [112] A5 [69] Suarez et al [70] Linares et al [71] Sasnauskas et al [72] AMDetector [73] RERAN [15] Yang et al …”

Section: Which Test Levels Are Addressed?mentioning

confidence: 99%

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Automated Testing of Android Apps: A Systematic Literature Review

et al. 2019

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Automated testing of Android apps is essential for app users, app developers and market maintainer communities alike. Given the widespread adoption of Android and the specificities of its development model, the literature has proposed various testing approaches for ensuring that not only functional requirements but also non-functional requirements are satisfied. In this paper, we aim at providing a clear overview of the state-of-the-art works around the topic of Android app testing, in an attempt to highlight the main trends, pinpoint the main methodologies applied and enumerate the challenges faced by the Android testing approaches as well as the directions where the community effort is still needed. To this end, we conduct a Systematic Literature Review (SLR) during which we eventually identified 103 relevant research papers published in leading conferences and journals until 2016. Our thorough examination of the relevant literature has led to several findings and highlighted the challenges that Android testing researchers should strive to address in the future. After that, we further propose a few concrete research directions where testing approaches are needed to solve recurrent issues in app updates, continuous increases of app sizes, as well as the Android ecosystem fragmentation. 1 INTRODUCTION Android smart devices have become pervasive after gaining tremendous popularity in recent years. As of July 2017, Google Play, the official app store, is distributing over 3 million Android applications (i.e., apps), covering over 30 categories ranging from entertainment and personalisation apps to education and financial apps. Such popularity among developer communities can be attributed to the accessible development environment based on familiar Java programming language as well as the availability of libraries implementing diverse functionalities [1]. The app distribution ecosystem around the official store and other alternative stores such as Anzhi and AppChina is further attractive for users to find apps and organisations to market their apps [2]. Unfortunately, the distribution ecosystem of Android is porous to poorly-tested apps [3]-[5]. Yet, as reported • ξ The corresponding author.

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Section: Test Methodologiesmentioning

confidence: 99%

Section: Which Test Levels Are Addressed?mentioning

confidence: 99%

Automated Testing of Android Apps: A Systematic Literature Review

et al. 2019

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“…There is a bottleneck in continuous deployment to the proprietary application servers of Android Play or Apple's App Store, which imposes a bottleneck on experimentation for mobile apps. Lettner et al [131] and Adinata and Liem [143] have developed libraries that load new user interfaces at run time, which would otherwise (at the time of writing in year 2013 and 2014 respectively) require a new deployment on Android Play. Xu et al [96] at LinkedIn instead advocate the use of quasi-experimental designs.…”

Section: Domain Specific Challengesmentioning

confidence: 99%

Controlled Experimentation in Continuous Experimentation: Knowledge and Challenges

Auer¹,

Ros²,

Kaltenbrunner³

et al. 2021

Preprint

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Context: Continuous experimentation and A/B testing is an established industry practice that has been researched for more than 10 years. Our aim is to synthesize the conducted research.Objective: We wanted to find the core constituents of a framework for continuous experimentation and the solutions that are applied within the field. Finally, we were interested in the challenges and benefits reported of continuous experimentation.Method: We applied forward snowballing on a known set of papers and identified a total of 128 relevant papers. Based on this set of papers we performed two qualitative narrative syntheses and a thematic synthesis to answer the research questions.Results: The framework constituents for continuous experimentation include experimentation processes as well as supportive technical and organizational infrastructure. The solutions found in the literature were synthesized to nine themes, e.g. experiment design, automated experiments, or metric specification. Concerning the challenges of continuous experimentation, the analysis identified cultural, organizational, business, technical, statistical, ethical, and domain-specific challenges. Further, the study concludes that the benefits of experimentation are mostly implicit in the studies.Conclusions: The research on continuous experimentation has yielded a large body of knowledge on experimentation. The synthesis of published research presented within include recommended infrastructure and experimentation process models, guidelines to mitigate the identified challenges, and what problems the various published solutions solve.

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Validation of A/B tests using Machine Data Learning Techniques

Kumar

2021

2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques

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A/B test tools of native mobile application

Cited by 4 publications

References 3 publications

Automated Testing of Android Apps: A Systematic Literature Review

Automated Testing of Android Apps: A Systematic Literature Review

Controlled Experimentation in Continuous Experimentation: Knowledge and Challenges

Validation of A/B tests using Machine Data Learning Techniques

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