Combining Automated GUI Exploration of Android apps with Capture and Replay through Machine Learning

Amalfitano, Domenico; Riccio, Vincenzo; Amatucci, Nicola; Simone, Vincenzo De; Fasolino, Anna Rita

doi:10.1016/j.infsof.2018.08.007

Cited by 27 publications

(17 citation statements)

References 12 publications

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“…Dynodroid [31] extends the random selection using weights and frequencies of events. Modeland search-based strategies such as COBWEB [39], SwiftHand [40], EHB-Droid [41], PUMA [42], EvoDroid [43], DroidBot [44], MobiGUITAR [45], juGULAR [46], ABE [47], Humanoid [48], Sapienz [1], Stoat [32], and MATE [9] apply model-based testing with dedicated search strategies to mobile applications. The type of model and search strategy is different in each approach and can range from simple to sophisticated solutions.…”

Section: Related Workmentioning

confidence: 99%

A comprehensive empirical evaluation of generating test suites for mobile applications with diversity

Vogel¹,

Tran²,

Grunske³

2021

Information and Software Technology

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Context: 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 trial-and-error basis, which may lead to better results for a specific problem. We consider the problem of generating test suites for mobile applications (apps) and rely on Sapienz, a state-of-the-art approach to this problem that uses a popular heuristic (NSGA-II) with a default configuration. Objective: We want to achieve better results in generating test suites with Sapienz while avoiding trial-and-error experiments to identify a more suitable configuration of Sapienz. Method: We conducted a fitness landscape analysis of Sapienz to analytically understand the search problem, which allowed us to make informed decisions about the heuristic and configuration of Sapienz when developing Sapienz div. We comprehensively evaluated Sapienz div in a headto-head comparison with Sapienz on 34 apps. Results: Analyzing the fitness landscape of Sapienz, we observed a lack of diversity of the evolved test suites and a stagnation of the search after 25 generations. Sapienz div realizes mechanisms that preserve the diversity of the test suites being evolved. The evaluation showed that Sapienz div achieves better or at least similar test results than Sapienz concerning coverage and the number of revealed faults. However, Sapienz div typically produces longer test sequences and requires more execution time than Sapienz. Conclusions: The understanding of the search problem obtained by the fitness landscape analysis helped us to find a more suitable configuration of Sapienz without trial-and-error experiments. By promoting diversity of test suites during the search, improved or at least similar test results in terms of faults and coverage can be achieved.

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Section: Related Workmentioning

confidence: 99%

A comprehensive empirical evaluation of generating test suites for mobile applications with diversity

Vogel¹,

Tran²,

Grunske³

2021

Information and Software Technology

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“…The goal of the tool and technique was to generate test cases that exercise the functionalities of the SUT by navigating the SUT GUI. Amalfitano et al [14] presented a technique for detecting and unlocking something they call a Gate GUI, using machine learning. A Gate GUI is, according to Amalfitano et al, a GUI that prevents a random exploration technique from advancing to relevant parts of the SUT.…”

Section: Related Workmentioning

confidence: 99%

On the Industrial Applicability of Augmented Testing: An Empirical Study

Nass

Alégroth

Feldt

2020

2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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Testing applications with graphical user Interfaces (GUI) is an important but also a time-consuming task in practice. Tools and frameworks for GUI test automation can make the test execution more efficient and lower the manual labor required for regression testing. However, the test scripts used for automated GUI-based testing still require a substantial development effort and are often reported as sensitive to change, leading to frequent and costly maintenance. The efficiency of development, maintenance, and evolution of such tests are thereby dependent on the readability of scripts and the ease-of-use of test tools/frameworks in which the test scripts are defined. To address these shortcomings in existing state-of-practice techniques, a novel technique referred to as Augmented Testing (AT) has been proposed. AT is defined as testing the System Under Test (SUT) through an Augmented GUI that superimposes information on top of the SUT GUI. The Augmented GUI can provide the user with hints, test data, or other support while also observing and recording the tester's interactions. For this study, a prototype tool, called Scout, has been used that adheres to the AT concept that is evaluated in an industrial empirical study. In the evaluation, quasi-experiments and questionnaire surveys are performed in two workshops, with 12 practitioners from two Swedish companies (Ericsson and Inceptive). Results show that Scout can be used to create equivalent test cases faster, with statistical significance, than creating automated scripts in two popular state-of-practice tools. The study concludes that AT has cost-value benefits, applies to industrial-grade software, and overcomes several deficiencies of state-of-practice GUI testing technologies in terms of ease-of-use.

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“…A recent testing technique that is GUI exploration based is introduced by Amalfitano et al [30]. e approach aims to exploit human involvement in the automated process to address the challenge of exploring Gate GUIs, which require to be exercised with particular input event sequences.…”

Section: Related Workmentioning

confidence: 99%

Porting Mobile Apps from iOS to Android: A Practical Experience

Lamhaddab

Lachgar

Elbaamrani

2019

Mobile Information Systems

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The recent rise of smartphones has triggered a revolution in mobile development. As a result of this incremental mobile innovation, new software engineering techniques, software documentation, and tools adapted to the mobile platform remain essential in order to help developers to better understand, analyze, and bootstrap porting mobile applications. In this paper, the authors propose a model-driven reverse-engineering approach based on static analysis, which describes a semantic metamodel of the iOS mobile application and extract design information (such as user interfaces, activity diagram, entities, framework and library dependencies) in order to generate the functional specification documentation and the Android UI skeleton. Thus, aiding the project team, who has in charge porting the app to another mobile platform, to agree upon a consensus on what has to be implemented and safe development cost by auto generating the Android UI skeleton project. To experiment this approach, the authors have implemented a tool called iSpecSnapshot. Moreover, they evaluate the performance of iSpecSnapshot by an experiment involving iOS applications that are ported to Android platform.

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Combining Automated GUI Exploration of Android apps with Capture and Replay through Machine Learning

Cited by 27 publications

References 12 publications

A comprehensive empirical evaluation of generating test suites for mobile applications with diversity

A comprehensive empirical evaluation of generating test suites for mobile applications with diversity

On the Industrial Applicability of Augmented Testing: An Empirical Study

Porting Mobile Apps from iOS to Android: A Practical Experience

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