@Article{SantiagoJśniorÖzcaBale:2022:MaTeCa,
               author = "Santiago J{\'u}nior, Valdivino Alexandre de and {\"O}zcan, Ender 
                         and Balera, Juliana Marino",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {University 
                         of Nottingham} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Many-objective test case generation for graphical user interface 
                         applications via search-based and model-based testing",
              journal = "Expert Systems with Applications",
                 year = "2022",
               volume = "208",
                pages = "e118075",
                month = "Dec.",
             keywords = "Graphical user interface, Many-objective optimisation, 
                         Metaheuristics and hyper-heuristics, Model-based testing, 
                         Search-based software testing.",
             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.",
                  doi = "10.1016/j.eswa.2022.118075",
                  url = "http://dx.doi.org/10.1016/j.eswa.2022.118075",
                 issn = "0957-4174",
             language = "en",
           targetfile = "1-s2.0-S0957417422012775-main.pdf",
        urlaccessdate = "03 maio 2024"
}