Publications of Rick Rabiser [rss]
2021
[115] A Domain Analysis of Resource and Requirements Monitoring: Towards a Comprehensive Model of the Software Monitoring Domain (Rick Rabiser, Klaus Schmid, Holger Eichelberger, Michael Vierhauser, Paul Grünbacher), In Software Engineering 2021 (Anne Koziolek, Ina Schaefer, Christoph Seidl, eds.), Gesellschaft für Informatik e.V., 2021. [bib] [doi]
[114] Evolution in Dynamic Software Product Lines (Clément Quinton, Michael Vierhauser, Rick Rabiser, Luciano Baresi, Paul Grünbacher, Christian Schumayer), In Journal of Software: Evolution and Process, John Wiley & Sons, volume 33, 2021. [bib] [doi]
2019
[113] Feature Modeling vs. Decision Modeling: History, Comparison and Perspectives (Rick Rabiser), ACM, 2019. [bib] [doi]
[112] Industrial and Academic Software Product Line Research at SPLC: Perceptions of the Community (Rick Rabiser, Klaus Schmid, Martin Becker, Goetz Botterweck, Matthias Galster, Iris Groher, Danny Weyns), In 23rd International Systems and Software Product Line Conference, 2019. [bib] [doi]
[111] A Domain Analysis of Resource and Requirements Monitoring: Towards a Comprehensive Model of the Software Monitoring Domain (Rick Rabiser, Klaus Schmid, Holger Eichelberger, Michael Vierhauser, Sam Guinea, Paul Grünbacher), In Information and Software Technology, volume 111, 2019. [bib] [doi]
[110] Developing and Evolving a DSL-based Approach for Runtime Monitoring of Systems of Systems (Rick Rabiser, Jürgen Thanhofer-Pilisch, Michael Vierhauser, Paul Grünbacher, Alexander Egyed), In Software Engineering and Software Management (SE), Stuttgart, Germany, 2019. [bib] [pdf] [doi]
[109] Towards Modeling Variability of Products, Processes, and Resources in CPPS (Kristof Meixner, Rick Rabiser, Stefan Biffl), ACM, 2019. [bib] [doi]
[108] A User Study on the Usefulness of Visualization Support for Requirements Monitoring (Lisa Maria Kritzinger, Thomas Krismayer, Rick Rabiser, Paul Grünbacher), In 7th IEEE Working Conference on Software Visualization, IEEE, 2019. [bib] [doi]
[107] Comparing Constraints Mined From Execution Logs to Understand Software Evolution (Thomas Krismayer, Michael Vierhauser, Rick Rabiser, Paul Grünbacher), In Proceedings of the 35th IEEE International Conference on Software Maintenance and Evolution (ICSME), 2019. [bib] [doi]
[106] A Constraint Mining Approach to Support Monitoring Cyber-Physical Systems (Thomas Krismayer, Rick Rabiser, Paul Grünbacher), In Proceedings of the 31st International Conference on Advanced Information Systems Engineering (CAiSE) (Paolo Giorgini, Barbara Weber, eds.), Springer International Publishing, volume 11483, 2019. [bib] [doi]
[105] Mining Constraints for Monitoring Systems of Systems (Thomas Krismayer, Rick Rabiser, Paul Grünbacher), In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, ACM, 2019. [bib] [doi]
[104] Using Constraint Mining to Analyze Software Development Processes (Thomas Krismayer, Christoph Mayr-Dorn, Johann Tuder, Rick Rabiser, Paul Grünbacher), In Proceedings of the International Conference on Software and Systems Process, IEEE, 2019. [bib] [doi]
[103] ReMinds-CMT: An Interactive Tool Supporting Constraint Mining for Requirements Monitoring (Thomas Krismayer, Peter Kronberger, Rick Rabiser, Paul Grünbacher), 2019. [bib]
[102] Supporting the Selection of Constraints for Requirements Monitoring from Automatically Mined Constraint Candidates (Thomas Krismayer, Peter Kronberger, Rick Rabiser, Paul Grünbacher), In Proceedings of the 25th International Working Conference on Requirements Engineering: Foundation for Software Quality, 2019. [bib] [doi]
[101] Introduction to the special issue on quality engineering and management of software-intensive systems (Michael Felderer, Helena Holmström Olsson, Rick Rabiser), volume 149, 2019. [bib] [doi]
[100] Using constraint mining to analyze software development processes (Thomas Krismayer, Christoph Mayr-Dorn, Johann Tuder, Rick Rabiser, Paul Grünbacher), In Proceedings of the International Conference on Software and System Processes, ICSSP 2019, Montreal, QC, Canada, May 25-26, 2019 (Stanley M. Sutton Jr., Ove Armbrust, Regina Hebig, eds.), IEEE / ACM, 2019. [bib] [doi]
[99] SPLC '19: Proceedings of the 23rd International Systems and Software Product Line Conference - Volume B (Carlos Cetina, Oscar Díaz, Laurence Duchien, Marianne Huchard, Rick Rabiser, Camille Salinesi, Christoph Seidl, Xhevahire Tërnava, Leopoldo Teixeira, Thomas Thüm, Tewfik Ziadi), ACM, 2019. [bib]
[98] First International Workshop on Languages for Modelling Variability (MODEVAR 2019) (David Benavides, Rick Rabiser, Don S. Batory, Mathieu Acher), ACM, 2019. [bib] [doi]
[97] Fourth International Workshop on Languages for Modelling Variability (SPLTea 2019) (Mathieu Acher, Rick Rabiser, Roberto E. Lopez-Herrejon), ACM, 2019. [bib] [doi]
2018
[96] Supporting Diagnosis of Requirements Violations in Systems of Systems (Michael Vierhauser, Jane Cleland-Huang, Rick Rabiser, Thomas Krismayer, Paul Grünbacher), In 26th IEEE International Requirements Engineering Conference, 2018. [bib] [doi]
[95] Monitoring CPS at Runtime -- A Case Study in the UAV Domain (Michael Vierhauser, Jane Cleland-Huang, Sean Bayley, Thomas Krismayer, Rick Rabiser, Paul Grünbacher), In Proceedings of the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), IEEE, 2018. [bib] [doi]
[94] Developing and evolving a DSL-based approach for runtime monitoring of systems of systems (Rick Rabiser, Jürgen Thanhofer-Pilisch, Michael Vierhauser, Paul Grünbacher, Alexander Egyed), In Automated Software Engineering, volume 25, 2018. [bib] [pdf] [doi]
[93] A Study and Comparison of Industrial vs. Academic Software Product Line Research Published at SPLC (Rick Rabiser, Klaus Schmid, Martin Becker, Goetz Botterweck, Matthias Galster, Iris Groher, Danny Weyns), In 22nd International Systems and Software Product Line Conference (SPLC 2018), 2018. [bib] [doi]
[92] A Comparison Framework for Runtime Monitoring Approaches (Rick Rabiser, Sam Guinea, Michael Vierhauser, Luciano Baresi, Paul Grünbacher), IEEE, 2018. [bib] [doi]
[91] Predicting User Demographics from Music Listening Information (Thomas Krismayer, Markus Schedl, Peter Knees, Rick Rabiser), In Multimedia Tools and Applications, 2018. [bib] [doi]
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