Herbold, Steffen

[["dc.bibliographiccitation.firstpage","271"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","International Journal on Software Tools for Technology Transfer"],["dc.bibliographiccitation.lastpage","279"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Herbold, Steffen"],["dc.contributor.author","Hoffmann, Andrea"],["dc.date.accessioned","2018-11-07T10:23:30Z"],["dc.date.available","2018-11-07T10:23:30Z"],["dc.date.issued","2017"],["dc.description.abstract","The quality of Web services is an important factor for businesses that advertise or sell their services in the Internet. Failures can directly lead to fewer costumers or security problems. However, the testing of complex Web services that are organized in service-oriented architectures is a difficult and complex problem. Model-based testing (MBT) is one solution to deal with the complexity of the testing. With MBT, testers do not define the tests directly, but rather specify the structure and behavior of the System Under Test using models. Then, a test strategy is used to derive test cases automatically from the models. However, MBT yields a large amount of tests for complex systems which require lots of resources for their execution, thereby limiting its potential. Within this article, we discuss how cloud computing can be used to provide the required resources for scaling up test campaigns with large amounts of test cases derived using MBT."],["dc.description.sponsorship","MIDAS European project [318786]"],["dc.identifier.doi","10.1007/s10009-017-0449-2"],["dc.identifier.isi","000400981200001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14375"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/42471"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation.issn","1433-2787"],["dc.relation.issn","1433-2779"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Model-based testing as a service"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","812"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Empirical Software Engineering"],["dc.bibliographiccitation.lastpage","841"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Herbold, Steffen"],["dc.contributor.author","Grabowski, Jens"],["dc.contributor.author","Waack, Stephan"],["dc.date.accessioned","2018-11-07T08:49:37Z"],["dc.date.available","2018-11-07T08:49:37Z"],["dc.date.issued","2011"],["dc.description.abstract","In this article, we present a novel algorithmic method for the calculation of thresholds for a metric set. To this aim, machine learning and data mining techniques are utilized. We define a data-driven methodology that can be used for efficiency optimization of existing metric sets, for the simplification of complex classification models, and for the calculation of thresholds for a metric set in an environment where no metric set yet exists. The methodology is independent of the metric set and therefore also independent of any language, paradigm or abstraction level. In four case studies performed on large-scale open-source software metric sets for C functions, C+ +, C# methods and Java classes are optimized and the methodology is validated."],["dc.identifier.doi","10.1007/s10664-011-9162-z"],["dc.identifier.isi","000294819700004"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7156"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/21509"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","1382-3256"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Calculation and optimization of thresholds for sets of software metrics"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]