Herbold, Steffen

[["dc.bibliographiccitation.firstpage","110802"],["dc.bibliographiccitation.journal","Journal of Systems and Software"],["dc.bibliographiccitation.volume","171"],["dc.contributor.author","Herbold, Steffen"],["dc.contributor.author","Amirfallah, Aynur"],["dc.contributor.author","Trautsch, Fabian"],["dc.contributor.author","Grabowski, Jens"],["dc.date.accessioned","2021-04-14T08:30:27Z"],["dc.date.available","2021-04-14T08:30:27Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1016/j.jss.2020.110802"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83240"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.issn","0164-1212"],["dc.title","A systematic mapping study of developer social network research"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1866"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Empirical Software Engineering"],["dc.bibliographiccitation.lastpage","1902"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Herbold, Steffen"],["dc.contributor.author","Trautsch, Alexander"],["dc.contributor.author","Grabowski, Jens"],["dc.date.accessioned","2020-12-10T14:11:32Z"],["dc.date.available","2020-12-10T14:11:32Z"],["dc.date.issued","2016"],["dc.identifier.doi","10.1007/s10664-016-9468-y"],["dc.identifier.eissn","1573-7616"],["dc.identifier.issn","1382-3256"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/71101"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Global vs. local models for cross-project defect prediction"],["dc.title.alternative","A replication study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","127"],["dc.bibliographiccitation.lastpage","138"],["dc.contributor.author","Trautsch, Alexander Richard"],["dc.contributor.author","Herbold, Steffen"],["dc.contributor.author","Grabowski, Jens"],["dc.contributor.orcid","0000-0001-5236-7953"],["dc.creator.author","Trautsch, Alexander"],["dc.date.accessioned","2020-12-02T07:47:36Z"],["dc.date.available","2020-12-02T07:47:36Z"],["dc.date.issued","2020"],["dc.description.abstract","Software quality evolution and predictive models to support decisions about resource distribution in software quality assurance tasks are an important part of software engineering research. Recently, a fine-grained just-in-time defect prediction approach was proposed which has the ability to find bug-inducing files within changes instead of only complete changes. In this work, we utilize this approach and improve it in multiple places: data collection, labeling and features. We include manually validated issue types, an improved SZZ algorithm which discards comments, whitespaces and refactorings. Additionally, we include static source code metrics as well as static analysis warnings and warning density derived metrics as features. To assess whether we can save cost we incorporate a specialized defect prediction cost model. To evaluate our proposed improvements of the fine-grained just-in-time defect prediction approach we conduct a case study that encompasses 38 Java projects, 492,241 file changes in 73,598 commits and spans 15 years. We find that static source code metrics and static analysis warnings are correlated with bugs and that they can improve the quality and cost saving potential of just-in-time defect prediction models."],["dc.identifier.doi","10.1109/icsme46990.2020.00022"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69417"],["dc.language.iso","en"],["dc.publisher","IEEE"],["dc.relation.conference","International Conference on Software Maintenance and Evolution"],["dc.relation.doi","10.1109/ICSME46990.2020"],["dc.relation.eventend","2020-10-03"],["dc.relation.eventlocation","Adelaide"],["dc.relation.eventstart","2020-09-27"],["dc.relation.isbn","978-1-7281-5619-4"],["dc.relation.ispartof","2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)"],["dc.relation.orgunit","Institut für Informatik"],["dc.title","Static source code metrics and static analysis warnings for fine-grained just-in-time defect prediction"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.status","accepted"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","309"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","International Journal on Software Tools for Technology Transfer"],["dc.bibliographiccitation.lastpage","324"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Herbold, Steffen"],["dc.contributor.author","Harms, Patrick"],["dc.contributor.author","Grabowski, Jens"],["dc.date.accessioned","2018-11-07T10:23:31Z"],["dc.date.available","2018-11-07T10:23:31Z"],["dc.date.issued","2017"],["dc.description.abstract","Usage-based testing focuses quality assurance on highly used parts of the software. The basis for this are usage profiles based on which test cases are generated. There are two fundamental approaches in usage-based testing for deriving usage profiles: either the system under test (SUT) is observed during its operation and from the obtained usage data a usage profile is automatically inferred, or a usage profile is modeled by hand within a model-based testing (MBT) approach. In this article, we propose a third and combined approach, where we automatically infer a usage profile and create a test data repository from usage data. Then, we create representations of the generated tests and test data in the test model from an MBT approach. The test model enables us to generate executable Testing and Test Control Notation version 3 (TTCN-3) and thereby allows us to automate the test execution. Together with industrial partners, we adopted this approach in two pilot studies. Our findings show that usage-based testing can be applied in practice and greatly helps with the automation of tests. Moreover, we found that even if usage-based testing is not of interest, the incorporation of usage data can ease the application of MBT."],["dc.identifier.doi","10.1007/s10009-016-0437-y"],["dc.identifier.fs","626812"],["dc.identifier.isi","000400981200003"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/42473"],["dc.identifier.url","http://rdcu.be/v5cZ"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation.issn","1433-2787"],["dc.relation.issn","1433-2779"],["dc.title","Combining usage-based and model-based testing for service-oriented architectures in the industrial practice"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","811"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","IEEE Transactions on Software Engineering"],["dc.bibliographiccitation.lastpage","833"],["dc.bibliographiccitation.volume","44"],["dc.contributor.author","Herbold, Steffen"],["dc.contributor.author","Trautsch, Alexander"],["dc.contributor.author","Grabowski, Jens"],["dc.date.accessioned","2020-12-10T18:26:23Z"],["dc.date.available","2020-12-10T18:26:23Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1109/TSE.2017.2724538"],["dc.identifier.eissn","1939-3520"],["dc.identifier.eissn","2326-3881"],["dc.identifier.issn","0098-5589"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76062"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation.haserratum","/handle/2/76064"],["dc.title","A Comparative Study to Benchmark Cross-Project Defect Prediction Approaches"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1036"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Empirical Software Engineering"],["dc.bibliographiccitation.lastpage","1083"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Trautsch, Fabian"],["dc.contributor.author","Herbold, Steffen"],["dc.contributor.author","Makedonski, Philip"],["dc.contributor.author","Grabowski, Jens"],["dc.date.accessioned","2019-07-24T15:05:15Z"],["dc.date.available","2019-07-24T15:05:15Z"],["dc.date.issued","2017"],["dc.description.abstract","The usage of empirical methods has grown common in software engineering. This trend spawned hundreds of publications, whose results are helping to understand and improve the software development process. Due to the data-driven nature of this venue of investigation, we identified several problems within the current state-of-the-art that pose a threat to the replicability and validity of approaches. The heavy re-use of data sets in many studies may invalidate the results in case problems with the data itself are identified. Moreover, for many studies data and/or the implementations are not available, which hinders a replication of the results and, thereby, decreases the comparability between studies. Furthermore, many studies use small data sets, which comprise of less than 10 projects. This poses a threat especially to the external validity of these studies. Even if all information about the studies is available, the diversity of the used tooling can make their replication even then very hard. Within this paper, we discuss a potential solution to these problems through a cloud-based platform that integrates data collection and analytics. We created SmartSHARK, which implements our approach. Using SmartSHARK, we collected data from several projects and created different analytic examples. Within this article, we present SmartSHARK and discuss our experiences regarding the use of it and the mentioned problems. Additionally, we show how we have addressed the issues that we have identified during our work with SmartSHARK."],["dc.identifier.doi","10.1007/s10664-017-9537-x"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62035"],["dc.language.iso","en"],["dc.relation.issn","1382-3256"],["dc.relation.issn","1573-7616"],["dc.title","Addressing problems with replicability and validity of repository mining studies through a smart data platform"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","450"],["dc.bibliographiccitation.issue","3-4"],["dc.bibliographiccitation.journal","International Journal on Advances in Intelligent Systems"],["dc.bibliographiccitation.lastpage","467"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Harms, Patrick"],["dc.contributor.author","Herbold, Steffen"],["dc.contributor.author","Grabowski, Jens"],["dc.date.accessioned","2019-07-24T14:31:52Z"],["dc.date.available","2019-07-24T14:31:52Z"],["dc.date.issued","2014"],["dc.description.abstract","Task trees are a well-known way for the manual modeling of user interactions. They provide an ideal basis for software analysis including usability evaluations if they are generated based on usage traces. In this paper, we present an approach for the automated generation of task trees based on traces of user interactions. For this, we utilize usage monitors to record all events caused by users. These events are written into log files from which we generate task trees. The generation mechanism covers the detection of iterations, of common usage sequences, and the merging of similar variants of semantically equal sequence. We validate our method in two case studies and show that it is able to generate task trees representing actual user behavior."],["dc.identifier.fs","626751"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62030"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.issn","1942-2679"],["dc.title","Extended Trace-based Task Tree Generation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","327"],["dc.bibliographiccitation.journal","Advances in Computers"],["dc.bibliographiccitation.lastpage","367"],["dc.bibliographiccitation.volume","85"],["dc.contributor.author","Herbold, Steffen"],["dc.contributor.author","Buenting, Uwe"],["dc.contributor.author","Grabowski, Jens"],["dc.contributor.author","Waack, Stephan"],["dc.date.accessioned","2018-11-07T09:14:52Z"],["dc.date.available","2018-11-07T09:14:52Z"],["dc.date.issued","2012"],["dc.description.abstract","End-user software systems are usually operated using a Graphical User Interface (GUI). Therefore, the quality of the GUI greatly impacts the quality of use of a software, which makes GUI testing an important part of software quality assurance. Furthermore, bugs in the software are triggered by the users through interaction with the software's GUI. Thus, reliable usage information is required to replicate bugs, which is the first important step toward bug fixing. To support both GUI testing and bug reporting, we propose a GUI execution capturing technique that can be integrated into and deployed with software products. Our capturing technique not only captures user actions but also the internal communication between GUI objects. The captured internal communication allows further analysis of the software for debugging. Additionally, we propose a replaying mechanism based on the captures. The replay utilizes the internal communication to abstract from screen coordinates. The feasibility of both techniques is demonstrated through proof-of-concept implementations. In a case study, we applied our techniques to three software projects and evaluated the capabilities of both the approaches. We show that the integration of the capturing has no significant effort, and the whole methodology is mature enough to be integrated into the nightly test cycle of an industrial software project."],["dc.identifier.doi","10.1016/B978-0-12-396526-4.00007-2"],["dc.identifier.isi","000306762800007"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27529"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.isbn","978-0-12-396526-4"],["dc.relation.issn","0065-2458"],["dc.title","Deployable Capture/Replay Supported by Internal Messages"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["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"]]

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