Markus Schnappinger, M.Sc.
Technische Universität München
Informatik 4 - Lehrstuhl für Software & Systems Engineering (Prof. Pretschner)
85748 Garching b. München
- Tel.: +49 (89) 289 - 17386
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In research related to Software Quality, we are interested to create a dataset of source code and corresponding quality labels. Thus, we have built an online platform that allows to evaluate strategically chosen code snippets from various code bases. With just a small invest of your time, you can actively foster very important research. Curious how that looks like? https://coality.sse.in.tum.de/
Since 2018 I have been working as a PhD student at the Chair of Software and Systems Engineering (Prof. Dr. Pretschner). Prior to joining the chair, I studied Software Engineering in an elite graduate program hosted by the TU Munich in cooperation with the Ludwig-Maximilians University Munich and the University of Augsburg, which included internships at Capgemini and Lero - the irish software research institute. I am also an alumnus of the German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes) as well as the Lothar and Sigrid Rohde-Foundation.
My area of research is Software Quality. I am interested in processes to assess the non-functional quality of (large) software systems and aim to automate these processes or parts thereof. In the longshot, my goal is to establish software quality analyses that are fast, cheap, reliable and do not require human expert interaction. Hence, my daily work features Software Measurement, Machine Learning, Data Mining, Modeling, and Representations of Software Systems.
If you are interested in doing your thesis or guided research in the field of (non-functional) Software Quality and its automatic assessment, please feel free to contact me. As I work closely with industry, there are many opportunities for practical and interesting projects.
Open theses (application details in the pdf): ./-
- Cornering Cohesion: Investigating new ways to measure cohesion*
- Measuring cohesion and coupling: a comparison of different metrics and their usefulness for software quality analyses
- Mining Repositories for Quality Indicators*
- Quality Evaluation of Data Models*
- A Labeling Platform for Source Code*
- Vectorizing Software for Machine Learning*
- Requirements documentation and analysis for changes to existing business systems*
- Assessing the Quality of Code comments using machine learning*
- Identification of generated Code*
* in cooperation with itestra
Winter Semester '19/'20: Seminar Software Quality
Summer Semester '19: Requirements Engineering (Elite program)
Winter Semester '18/19: Practical Course Introduction to Programming
Summer Semester '18: Requirements Engineering (Elite program)