CAIN 2022
Mon 16 - Tue 17 May 2022
co-located with ICSE 2022
Michael Felderer

Registered user since Tue 16 Oct 2018

Name:Michael Felderer
Bio:

Michael Felderer is a professor at the Department of Computer Science at the University of Innsbruck, Austria and a senior researcher at the Department of Software Engineering at the Blekinge Institute of Technology, Sweden. In 2014 he was a guest researcher at Lund University, Sweden and in 2015 a guest lecturer at the University of Stuttgart, Germany. His fields of expertise and interest include software quality, testing, software and security processes, risk management, software analytics and measurement, requirements engineering, model-based software engineering and empirical research methodology in software and security engineering. Michael Felderer holds a habilitation degree from the University of Innsbruck, co-authored more than 130 publications and received 9 best paper awards. His research has a strong empirical focus also using methods of data science and is directed towards development and evaluation of efficient and effective methods to improve quality and value of industrial software systems and processes in close collaboration with companies. Michael Felderer himself has more than 10 years of industrial experience as a senior executive consultant, project manager and software engineer. He is an internationally recognized member of the software engineering research community and supports it as an editorial board member, organizer of conferences (e.g. General Chair of PROFES 2017) and regular PC member of premier conferences. For more information visit his website at mfelderer.at.

Country:Austria
Affiliation:University of Innsbruck
Personal website:http://mfelderer.at/
Research interests:Software Engineering, Security Engineering, Data Analytics

Contributions

SERP4IoT 2022 Committee Member in Program Committee within the SERP4IoT 2022-track
SE4RAI 2022 Programme Committee in Program Committee within the SE4RAI 2022-track
CAIN 2022 What is Software Quality for AI Engineers? Towards a Thinning of the Fog
Data Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based Systems
AST 2022 Going Online: Reflections on Testing Machine Learning Based Systems
TechDebt 2022 Austria in Program Committee within the Technical Papers-track
SEAMS 2022 Towards Model Co-evolution Across Self-Adaptation Steps for Combined Safety and Security Analysis
ICSE 2022 Social Science Theories in Software Engineering Research
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