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Using genetic programming to evolve action selection rules in traversal-based automated software testing: results obtained with the TESTAR tool

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Using genetic programming to evolve action selection rules in traversal-based automated software testing: results obtained with the TESTAR tool

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dc.contributor.author Esparcia Alcázar, Anna Isabel es_ES
dc.contributor.author Almenar-Pedrós, Francisco es_ES
dc.contributor.author Vos, Tanja Ernestina es_ES
dc.contributor.author Rueda Molina, Urko es_ES
dc.date.accessioned 2019-09-13T20:01:23Z
dc.date.available 2019-09-13T20:01:23Z
dc.date.issued 2018 es_ES
dc.identifier.issn 1865-9284 es_ES
dc.identifier.uri http://hdl.handle.net/10251/125658
dc.description.abstract [EN] Traversal-based automated software testing involves testing an application via its graphical user interface (GUI) and thereby taking the user's point of view and executing actions in a human-like manner. These actions are decided on the fly, as the software under test (SUT) is being run, as opposed to being set up in the form of a sequence prior to the testing, a sequence that is then used to exercise the SUT. In practice, random choice is commonly used to decide which action to execute at each state (a procedure commonly referred to as monkey testing), but a number of alternative mechanisms have also been proposed in the literature. Here we propose using genetic programming (GP) to evolve such an action selection strategy, defined as a list of IF-THEN rules. Genetic programming has proved to be suited for evolving all sorts of programs, and rules in particular, provided adequate primitives (functions and terminals) are defined. These primitives must aim to extract the most relevant information from the SUT and the dynamics of the testing process. We introduce a number of such primitives suited to the problem at hand and evaluate their usefulness based on various metrics. We carry out experiments and compare the results with those obtained by random selection and also by Q-learning, a reinforcement learning technique. Three applications are used as Software Under Test (SUT) in the experiments. The analysis shows the potential of GP to evolve action selection strategies. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Memetic Computing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Automated software testing via the GUI es_ES
dc.subject Genetic programming es_ES
dc.subject Action selection for testing es_ES
dc.subject Testing metrics es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Using genetic programming to evolve action selection rules in traversal-based automated software testing: results obtained with the TESTAR tool es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s12293-018-0263-8 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Esparcia Alcázar, AI.; Almenar-Pedrós, F.; Vos, TE.; Rueda Molina, U. (2018). Using genetic programming to evolve action selection rules in traversal-based automated software testing: results obtained with the TESTAR tool. Memetic Computing. 10(3):257-265. https://doi.org/10.1007/s12293-018-0263-8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1007/s12293-018-0263-8 es_ES
dc.description.upvformatpinicio 257 es_ES
dc.description.upvformatpfin 265 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 3 es_ES
dc.relation.pasarela S\369592 es_ES
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