Conference paper Open Access

Search-based Automated Play Testing of Computer Games: a model-based approach

Ferdous, Raihana; Kifetew, Fitsum; Prandi, Davide; Prasetya, I. S. W. B; Shirzadehhajimahmood, Samira; Susi, Angelo


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        <foaf:name>Prandi, Davide</foaf:name>
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        <foaf:name>Shirzadehhajimahmood, Samira</foaf:name>
        <foaf:givenName>Samira</foaf:givenName>
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            <foaf:name>Utrecht University, the Netherlands</foaf:name>
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        <foaf:name>Susi, Angelo</foaf:name>
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    <dct:title>Search-based Automated Play Testing of Computer Games: a model-based approach</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2021</dct:issued>
    <dcat:keyword>game play testing</dcat:keyword>
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    <dcat:keyword>model-based test- ing</dcat:keyword>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2021-07-27</dct:issued>
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    <dct:description>&lt;p&gt;Computergametechnologyisincreasinglymorecomplexand applied in a wide variety of domains, beyond entertainment, such as training and educational scenarios. Testing games is a difficult task re- quiring a lot of manual effort since the interaction space in the game is very fine grained and requires a certain level of intelligence that cannot be easily automated. This makes testing a costly activity in the overall development of games.&lt;/p&gt; &lt;p&gt;This paper presents a model-based formulation of game play testing in such a way that search-based testing can be applied for test generation. An abstraction of the desired game behaviour is captured in an extended finite state machine (EFSM) and search-based algorithms are used to derive abstract tests from the model, which are then concretised into action sequences that are executed on the game under test.&lt;/p&gt; &lt;p&gt;The approach is implemented in a prototype tool EvoMBT. We carried out experiments on a 3D game to assess the suitability of the approach in general, and search-based test generation in particular. We applied 5 search algorithms for test generation on three different models of the game. Results show that search algorithms are able to achieve reasonable coverage on models: between 75% and 100% for the small and medium sized models, and between 29% and 56% for the bigger model. Mutation analysis shows that on the actual game application tests kill up to 99% of mutants. Tests have also revealed previously unknown faults.&lt;/p&gt;</dct:description>
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