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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Most of my publications are listed at the bottom of this page and on dblp.
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. These studies 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), the Elite Network of Bavaria (Elitenetzwerk Bayern) 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, empirical studies, and investigating models for easier software comprehension.
More details about my main project is described here.
If you are interested in doing your thesis or guided research in the field of internal Software Quality and its automatic assessment, feel free to contact us. As I work closely with industry, there are many opportunities for practical and interesting projects.
Some open topics are listed below. If one of them appeals to you in particular, I'm looking forward to your email with attached grade report and short cv.
Open theses (application details in the pdf):
- Investigating Inter-Class Attributes for Capturing Software Maintainability
- Detecting Code Smells using Graph Neural Networks*
- Automatic Identification and Rating of the Usefulness of Source Code Comments*
- Mining Repositories for Quality Indicators*
- Using Text Classification and Image-based Learning to Predict Software Quality*
- Detecting Smells in Data Models*
- 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*
- Identifier Dictionaries*
- Configuration of Static Analysis Tools for Effective Bug Detection*
- A Super-Metric for Measuring Adequacy in the Context of Software Architecture and Software Programmingm
* in cooperation with itestra GmbH
m in cooperation with msg Systems AG
|Winter Semester 21/22||Seminar Software Quality|
|Summer Semester 2021||Seminar Software Quality |
Requirements Engineering (Elite program)
|Winter Semester 20/21||Seminar Software Quality|
|Summer Semester 20|| |
Requirements Engineering (Elite program)
Seminar Software Quality
|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)|
- Efficient Platform Migration of a Mainframe Legacy System Using Custom Transpilation. 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME), IEEE, 2021 mehr…
- Human-level Ordinal Maintainability Prediction Based on Static Code Metrics. Evaluation and Assessment in Software Engineering, ACM, 2021 mehr…
- Defining a Software Maintainability Dataset: Collecting, Aggregating and Analysing Expert Evaluations of Software Maintainability. 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), IEEE, 2020 mehr…
- A Software Maintainability Dataset. 2020 mehr…
- 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; Zachau, Simon, Fietzke, Arnaud; Pretschner, Alexander: A Preliminary Study on Using Text- and Image-Based Machine Learning to Predict Software Maintainability, 2022 Software Quality Days, Springer, 2022 (accepted and in press)
Elsner, Daniel; Würsching, Roland; Schnappinger, Markus; Pretschner, Alexander; Graber, Maria; Dammer, Rene; Reimer, Silke: Build System Aware Multi-language Regression Test Selection in Continuous Integration, IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice, 2022 (accepted and in press)