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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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. More details about my work is described here.
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): ./-
- Using Text Classification and Image-based Learning to Predict Software Quality*
- Mining Repositories for Quality Indicators*
- Quality Evaluation of Data Models*
- Cornering Cohesion: Investigating new ways to measure cohesion*
- Measuring cohesion and coupling: a comparison of different metrics and their usefulness for software quality analyses*
- 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)
- Learning a Classifier for Prediction of Maintainability Based on Static Analysis Tools. 2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC), IEEE, 2019 mehr…
- Software quality assessment in practice. Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement - ESEM '18, ACM Press, 2018 mehr…
Schnappinger, Markus; Fietzke, Arnaud; Pretschner, Alexander: "Defining a Software Maintainability Dataset: Collecting, Aggregating and Analysing Expert Evaluations of Software Maintainability", International Conference on Software Maintenance and Evolution (ICSME), 2020 (accepted, in press)
Schnappinger, Markus; Fietzke, Arnaud; Pretschner, Alexander: "A Software Maintainability Dataset", archived on Figshare.com. DOI: 0.6084/m9.figshare.12801215 Dataset