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dc.contributor.authorGatt, Cristina-
dc.contributor.authorBugeja, Mark-
dc.contributor.authorMicallef, Mark-
dc.date.accessioned2022-04-12T06:31:03Z-
dc.date.available2022-04-12T06:31:03Z-
dc.date.issued2022-
dc.identifier.citationGatt, C., Bugeja, M., & Bugeja, M. (2022). Towards domain-specific automated testing via behavioural cloning. International Workshop on AI in Software Testing (AIST).en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/93434-
dc.description.abstractWhen setting out a research roadmap for software testing, Bertolino [1] presented four dreams, one of which was 100% automatic testing. Fifteen years later, the dream has not been realised but the promise of artificial intelligence techniques brings us closer than ever before. In this paper, we propose that one way to achieve this goal is to leverage the commonalities that exist amongst domain-specific applications. That is to say that whilst every application within a particular domain is arguably unique, they all share a considerable overlap in terms of features. We propose an approach based on Behavioural Cloning, an AI technique whereby an agent observes traces by an expert and attempts to carry out domain-specific tasks in previously unseen contexts based on those traces. Using online stores as a case study, we discuss initial investigations into this idea, present results and identify a roadmap going forward.en_GB
dc.language.isoenen_GB
dc.publisherICSTen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectComputer software -- Verificationen_GB
dc.subjectComputer software -- Testingen_GB
dc.subjectSoftware engineering -- Case studiesen_GB
dc.subjectHuman-computer interactionen_GB
dc.subjectAutomation -- Human factorsen_GB
dc.subjectArtificial intelligence -- Computer programsen_GB
dc.titleTowards domain-specific automated testing via behavioural cloningen_GB
dc.typeconferenceObjecten_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.bibliographicCitation.conferencenameInternational Workshop on AI in Software Testing (AIST)en_GB
dc.bibliographicCitation.conferenceplaceVirtually, 04/04/2022en_GB
dc.description.reviewedpeer-revieweden_GB
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