Amplifying Tests for Cross-Platform Apps through Test Patterns

Assis, Thiago Botti de; Menegassi, André Augusto; Endo, André Takeshi

doi:10.18293/seke2019-076

Cited by 3 publications

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Preference-wise Testing of Android Apps via Test Amplification

Pan

Pei

et al. 2023

ACM Trans. Softw. Eng. Methodol.

View full text Add to dashboard Cite

Preferences, the setting options provided by Android, are an essential part of Android apps. Preferences allow users to change app features and behaviors dynamically, and therefore their impacts need to be considered when testing the apps. Unfortunately, few test cases explicitly specify the assignments of valid values to the preferences, or configurations , under which they should be executed, and few existing mobile testing tools take the impact of preferences into account or provide help to testers in identifying and setting up the configurations for running the tests. This paper presents the Prefest approach to effective testing of Android apps with preferences. Given an Android app and a set of test cases for the app, Prefest amplifies the test cases with a small number of configurations to exercise more behaviors and detect more bugs that are related to preferences. In an experimental evaluation conducted on real-world Android apps, amplified test cases produced by Prefest from automatically generated test cases covered significantly more code of the apps and detected 7 real bugs, and the tool’s test amplification time was at the same order of magnitude as the running time of the input test cases. Prefest ’s effectiveness and efficiency in amplifying programmer-written test cases was comparable with that in amplifying automatically generated test cases.

show abstract

Preference-wise Testing of Android Apps via Test Amplification

Pan

Pei

et al. 2023

ACM Trans. Softw. Eng. Methodol.

View full text Add to dashboard Cite

show abstract

Automatic test amplification for executable models

Khorram

Bousse

Mottu

et al. 2022

Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems

View full text Add to dashboard Cite

Behavioral models are important assets that must be thoroughly verified early in the design process. This can be achieved with manuallywritten test cases that embed carefully hand-picked domain-specific input data. However, such test cases may not always reach the desired level of quality, such as high coverage or being able to localize faults efficiently. Test amplification is an interesting emergent approach to improve a test suite by automatically generating new test cases out of existing manually-written ones. Yet, while ad-hoc test amplification solutions have been proposed for a few programming languages, no solution currently exists for amplifying the test cases of behavioral models.In this paper, we fill this gap with an automated and generic approach. Given an executable DSL, a conforming behavioral model, and an existing test suite, our approach generates new regression test cases in three steps: (i) generating new test inputs by applying a set of generic modifiers on the existing test inputs; (ii) running the model under test with new inputs and generating assertions from the execution traces; and (iii) selecting the new test cases that increase the mutation score. We provide tool support for the approach atop the Eclipse GEMOC Studio 1 and show its applicability in an empirical study. In the experiment, we applied the approach to 71 test suites written for models conforming to two different DSLs, and for 67 of the 71 cases, it successfully improved the mutation score between 3.17 % and 54.11 % depending on the initial setup. CCS CONCEPTS• Software and its engineering → Software testing and debugging; Model-driven software engineering.

show abstract

DeltaDroid : Dynamic Delivery Testing in Android

Ghorbani

Jabbarvand

Salehnamadi

et al. 2023

ACM Trans. Softw. Eng. Methodol.

View full text Add to dashboard Cite

Android is a highly fragmented platform with a diverse set of devices and users. To support the deployment of apps in such a heterogeneous setting, Android has introduced dynamic delivery —a new model of software deployment in which optional, device- or user-specific functionalities of an app, called Dynamic Feature Modules (DFMs) , can be installed, as needed, after the app’s initial installation. This model of app deployment, however, has exacerbated the challenges of properly testing Android apps. In this paper, we first describe the results of an extensive study in which we formalized a defect model representing the various conditions under which DFM installations may fail. We then present D elta D roid —a tool aimed at assisting the developers with validating dynamic delivery behavior in their apps by augmenting their existing test suite. Our experimental evaluation using real-world apps corroborates D elta D roid ’s ability to detect many crashes and unexpected behaviors that the existing automated testing tools cannot reveal.

show abstract

Amplifying Tests for Cross-Platform Apps through Test Patterns

Cited by 3 publications

References 15 publications

Preference-wise Testing of Android Apps via Test Amplification

Preference-wise Testing of Android Apps via Test Amplification

Automatic test amplification for executable models

DeltaDroid : Dynamic Delivery Testing in Android

Contact Info

Product

Resources

About