A Framework for Monkey GUI Testing

Wetzlmaier, Thomas; Ramler, Rudolf; Putschogl, Werner

doi:10.1109/icst.2016.51

Cited by 22 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [10], a monkey testing framework is presented which through its reusable components and extension points, enables development of customized monkeys for different application scenarios or technical requirements, e.g. requirements of the nightly build integration.…”

Section: Why Is It New?mentioning

confidence: 99%

Making Android apps monkey-friendly

Paydar

2020

Proceedings of the IEEE/ACM 7th International Conference on Mobile Software Engineering and Systems

View full text Add to dashboard Cite

Monkey testing is a random testing technique in which a stream of pseudo-random events are automatically fired on the GUI of the application under test, usually with the purpose of robustness testing or responsiveness analysis. A line of research is dedicated to addressing the limitations of monkey testing for Android apps. However, all the existing works try to improve the underlying algorithms or techniques used by the monkey testing tools. In this vision paper, we propose the idea of improving the effectiveness of monkey testing by automatically refactoring the application under test. We provide two sample scenarios in which this idea can be used to address limitations of monkey testing for Android applications. CCS CONCEPTS • Software and its engineering → Software verification and validation.

show abstract

Section: Why Is It New?mentioning

confidence: 99%

Making Android apps monkey-friendly

Paydar

2020

Proceedings of the IEEE/ACM 7th International Conference on Mobile Software Engineering and Systems

View full text Add to dashboard Cite

show abstract

“…Existing test input generation approaches are able to generate such events either randomly or systematically. In the first case, a random event is chosen at each GUI screen to navigate the app [20,3,44,41,28,31,27]. On the other hand, systematic exploration strategies select new actions to be executed based on the app GUI model [9,5,4,6,43,7,40,25,8,32].…”

Section: Related Workmentioning

confidence: 99%

“…The approach consists of generating and sending random events such as clicks, scroll, system-level events to the SUT in order to detect GUI bugs. Relying on the Monkey tool, many other random GUI exploration tools have been developed [3,44,41]. Adaptive Random Testing (ART) [27] is a different random testing approach.…”

Section: Random Exploration Strategymentioning

confidence: 99%

Automatic Test Set Generation for Event-Driven Systems in the Absence of Specifications Combining Testing with Model Inference

Novella

Tufo

Fiengo

2019

ITC

View full text Add to dashboard Cite

The growing dependency of human activities on software technologies is leading to the need for designing more and more accurate testing techniques to ensure the quality and reliability of software components. A recent literature review of software testing methodologies reveals that several new approaches, which differ in the way test inputs are generated to efficiently explore systems behaviour, have been proposed. This paper is concerned with the challenge of automatically generating test input sets for Event-Driven Systems (EDS) for which neither source code nor specifications are available, therefore we propose an innovative fully automatic testing with model learning technique. It basically involves active learning to automatically infer a behavioural model of the System Under Test (SUT) using tests as queries, generates further tests based on the learned model to systematically explore unseen parts of the subject system, and makes use of passive learning to refine the current model hypothesis as soon as an inconsistency is found with the observed behaviour. Our passive learning algorithm uses the basic steps of Evidence-Driven State Merging (EDSM) and introduces an effective heuristic for choosing the pair of states to merge to obtain the target machine. Finally, the effectiveness of the proposed testing technique is demonstrated within the context of event-based functional testing of Android Graphical User Interface (GUI) applications and compared with that of existing baseline approaches.

show abstract

“…In the domain of automated app testing, Google's Android Monkey tool is regarded as the state-of-the-practice for automated testing for the Android system [2]. Android Monkey takes a practical solution for GUI (graphical user interface) based testing: a random testing approach ("monkey testing") for generating random events [3,4]. Although the "monkey" approach is cost-effective, the unintelligent manner of testing leaves room for improvement.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Detection of Changes in Usage-Phases of a Mobile App

2020

View full text Add to dashboard Cite

Under the fierce competition and budget constraints, most mobile apps are launched without sufficient tests. Thus, there exists a great demand for automated app testing. Recent developments in various machine learning techniques have made automated app testing a promising alternative to manual testing. This work proposes novel approaches for one of the core functionalities of automated app testing: the detection of changes in usage-phases of a mobile app. Because of the flexibility of app development languages and the lack of standards, each mobile app is very different from other apps. Furthermore, the graphical user interfaces for similar functionalities are rarely consistent or similar. Thus, we propose methods detecting usage-phase changes through object recognition and metrics utilizing graphs and generative models. Contrary to the existing change detection methods requiring learning models, the proposed methods eliminate the burden of training models. This elimination of training is suitable for mobile app testing whose typical usage-phase is composed of less than 10 screenshots. Our experimental results on commercial mobile apps show promising improvement over the state-of-the-practice method based on SIFT (scale-invariant feature transform).

show abstract

A Framework for Monkey GUI Testing

Cited by 22 publications

References 8 publications

Making Android apps monkey-friendly

Making Android apps monkey-friendly

Automatic Test Set Generation for Event-Driven Systems in the Absence of Specifications Combining Testing with Model Inference

Unsupervised Detection of Changes in Usage-Phases of a Mobile App

Contact Info

Product

Resources

About