Mag. Michael Moser

Area Manager Software Science

Telefon: +43 50 343 814

michael.moser@scch.at

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Mag. Michael Moser leitet den Forschungsbereich Software Science am SCCH und verbindet langjährige Expertise in Software System Analysis mit einem klaren Fokus auf Künstliche Intelligenz.

Research Interests

Software reverse engineering, Software redocumentation, Static code analysis
Software modernization, Software migration, Legacy software systems
Domain-specific languages, Domain-specific modelling, Language Engineering
AI-Assisted Software Engineering

Key Facts

Area Manager Software Science, SCCH, Austria
Senior Research Project Manager, SCCH, Austria
Industrial Researcher and Project Manager, SCCH, Austria
Software Quality and Customer Support, COMNEON electronic technology GmbH
Studies of Informatics at Faculty of Electrical Engineering, Czech Technical University, Czech Republic
Business Informatics, Master Studies, Johannes Kepler University Linz

Honours and Awards

Best Poster Award: Towards Reverse Engineering of Intermediate Code for Documentation Generations, Michael Moser, Michael Pfeiffer and Josef Pichler, 24th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2017
GC Genius Award: 2nd place for Software platform for Point-of-Care-Analysis, Gesundheitscluster OÖ, Category Research Company, October 2011

Mag. Michael Moser

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Research Interests

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Honours and Awards

Publikationen

2024

Evaluating AI-based Code Segmentation for ABAP Programs in an Industrial Use Case

Industrial Experience Report on AI-Assisted Coding in Professional Software Development

Innovating Industry with Research: eknows and Sysparency

2023

Using AI-Based Code Completion for Domain-Specific Languages

Specification-based Test Case Generation for C++ Engineering Software

Iterative Design and Evaluation of a Low-Code Development Platform for Welding Robot Control

Leveraging and Evaluating Automatic Code Summarization for JPA Program Comprehension

2022

On the Creation and Maintenance of a Documentation Generator in an Applied Research Context

Towards Attribute Grammar Mining by Symbolic Execution

2021

eknows: Platform for Multi-Language Reverse Engineering and Documentation Generation

2020

Leveraging machine learning for software redocumentation – A eomprehensive comparison of methods in practice

Stepwise abstraction of high-level system specifications from source code

An early investigation of unit testing practices of component-based software systems

An integrated approach for power transformer modeling and manufacturing

Leveraging machine learning for software redocumentation

A systematic mapping study on best practices for domain-specific modeling

Transformation of java source code to base-level Abstract State Machine. Design and implementation

2019

Improving quality of data exchange files. An industrial case study

Extracting high-level system specifications from source code via abstract state machines

Deriving an optimal noise adding mechanism for privacy-preserving machine learning.