Skip to main content

Research Repository

Advanced Search

Many-objective test case generation for graphical user interface applications via search-based and model-based testing

de Santiago, Valdivino Alexandre; Özcan, Ender; Balera, Juliana Marino

Many-objective test case generation for graphical user interface applications via search-based and model-based testing Thumbnail


Authors

Valdivino Alexandre de Santiago

Profile Image

ENDER OZCAN ender.ozcan@nottingham.ac.uk
Professor of Computer Science and Operational Research

Juliana Marino Balera



Abstract

The majority of the studies that generate test cases for graphical user interface (GUI) applications are based on or address functional requirements only. In spite of the fact that interesting approaches have been proposed, they do not address functional and non-functional requirements of the GUI systems, and non-functional properties of the created test suites altogether to generate test cases. This is called a many-objective perspective where several desirable and different characteristics are considered together to generate the test cases. In this study, we show how to combine search-based (optimisation) with model-based testing to generate test cases for GUI applications taking into account the many-objective perspective. We rely on meta and hyper-heuristics and we address two particular issues (problems) considering code-driven and use case-driven GUI testing. As for the code-driven testing, we target desktop applications and automatically read the C++ source code of the system, translate it into an event flow graph (EFG), and use objective functions that are graph-based measures. As for the use case-driven testing, EFGs are created directly via use cases. A rigorous evaluation was performed using 32 problem instances where we considered three multi-objective evolutionary algorithms and six selection hyper-heuristics using those algorithms as low-level (meta)heuristics. The performance of the algorithms was compared based on five different indicators, and also a new Multi-Metric Indicator (MMI) utilising multiple indicators and providing a unique measure for all algorithms. Results show that the metaheuristics obtained better performances overall, particularly NSGA-II, while Choice Function was the most outstanding hyper-heuristic approach.

Journal Article Type Article
Acceptance Date Jul 3, 2022
Online Publication Date Jul 18, 2022
Publication Date Dec 1, 2022
Deposit Date Nov 21, 2022
Publicly Available Date Jul 19, 2023
Journal Expert Systems with Applications
Print ISSN 0957-4174
Publisher Elsevier BV
Peer Reviewed Peer Reviewed
Volume 208
Article Number 118075
DOI https://doi.org/10.1016/j.eswa.2022.118075
Keywords Artificial Intelligence; Computer Science Applications; General Engineering
Public URL https://nottingham-repository.worktribe.com/output/8857228
Publisher URL https://www.sciencedirect.com/science/article/abs/pii/S0957417422012775?via%3Dihub

Files





You might also like



Downloadable Citations