Mag. Michael Moser
Publikationen
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2024
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Innovating Industry with Research: eknows and Sysparency
Geist, V., Moser, M., Pichler, J., & Schnitzhofer, F. (2024). Innovating Industry with Research: eknows and Sysparency. IEEE Software, 1–7.
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2023
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Using AI-Based Code Completion for Domain-Specific Languages
Piereder, C., Fleck, G., Geist, V., Moser, M., & Pichler, J. (2023). Using AI-Based Code Completion for Domain-Specific Languages. Lecture Notes in Computer Science, 227–242.
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Specification-based Test Case Generation for C++ Engineering Software
Hamberger, P., Klammer, C., Luger, T., Moser, M., Pfeiffer, M., & Piereder, C. (2023). Specification-based Test Case Generation for C++ Engineering Software. 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME).
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Iterative Design and Evaluation of a Low-Code Development Platform for Welding Robot Control
Schenkenfelder, B., Moser, M., Pfeiffer, M., Pichler, J., Salomon, C., & Winterer, M. (2023). Iterative Design and Evaluation of a Low-Code Development Platform for Welding Robot Control. 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA).
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Leveraging and Evaluating Automatic Code Summarization for JPA Program Comprehension
Mayer, R., Moser, M., & Geist, V. (2023). Leveraging and Evaluating Automatic Code Summarization for JPA Program Comprehension. 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER).
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2022
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On the Creation and Maintenance of a Documentation Generator in an Applied Research Context
Dorninger, B., Moser, M., Pichler, J., Rappl, M., & Sautter, J. (2022). On the Creation and Maintenance of a Documentation Generator in an Applied Research Context. Database and Expert Systems Applications - DEXA 2022 Workshops, 129–140.
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Towards Attribute Grammar Mining by Symbolic Execution
Moser, M., Pichler, J., & Pointner, A. (2022). Towards Attribute Grammar Mining by Symbolic Execution. 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER).
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2021
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eknows: Platform for Multi-Language Reverse Engineering and Documentation Generation
Moser, M., & Pichler, J. (2021). eknows: Platform for Multi-Language Reverse Engineering and Documentation Generation. 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME).
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2020
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Leveraging machine learning for software redocumentation – A eomprehensive comparison of methods in practice
Geist, V., Moser, M., Pichler, J., Santos, R., & Wieser, V. (2020). Leveraging machine learning for software redocumentation—A comprehensive comparison of methods in practice. Software: Practice and Experience, 51(4), 798–823. Portico.
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Stepwise abstraction of high-level system specifications from source code
Ferrarotti, F., Moser, M., & Pichler, J. (2020). Stepwise abstraction of high-level system specifications from source code. Journal of Computer Languages, 60, 100996.
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An early investigation of unit testing practices of component-based software systems
Buchgeher, G., Fischer, S., Moser, M., & Pichler, J. (2020). An Early Investigation of Unit Testing Practices of Component-Based Software Systems. 2020 IEEE Workshop on Validation, Analysis and Evolution of Software Tests (VST).
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An integrated approach for power transformer modeling and manufacturing
Lettner, C., Moser, M., & Pichler, J. (2020). An integrated approach for power transformer modeling and manufacturing. Procedia Manufacturing, 42, 351–355.
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Leveraging machine learning for software redocumentation
Geist, V., Moser, M., Pichler, J., Beyer, S., & Pinzger, M. (2020). Leveraging Machine Learning for Software Redocumentation. 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER).
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2019
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Improving quality of data exchange files. An industrial case study
Fleck, G., Moser, M., & Pichler, J. (2019). Improving Quality of Data Exchange Files. An Industrial Case Study. Lecture Notes in Computer Science, 161–175.
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Extracting high-level system specifications from source code via abstract state machines
Ferrarotti, F., Pichler, J., Moser, M., & Buchgeher, G. (2019). Extracting High-Level System Specifications from Source Code via Abstract State Machines. Lecture Notes in Computer Science, 267–283.
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Deriving an optimal noise adding mechanism for privacy-preserving machine learning.
Kumar, M., Rossbory, M., Moser, B. A., & Freudenthaler, B. (2019). Deriving an Optimal Noise Adding Mechanism for Privacy-Preserving Machine Learning. Database and Expert Systems Applications, 108–118.