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2020 – today
- 2023
[b1]Markus Schnappinger:
On Machine Learning Assisted Software Maintainability Assessments. Technical University of Munich, Germany, 2023- [c9]Lena Gregor, Markus Schnappinger, Alexander Pretschner:
Revisiting Inter-Class Maintainability Indicators. SANER 2023: 805-814 - 2022
- [c8]Daniel Elsner, Roland Wuersching, Markus Schnappinger, Alexander Pretschner:
Probe-based Syscall Tracing for Efficient and Practical File-level Test Traces. AST@ICSE 2022: 126-137 - [c7]Daniel Elsner, Roland Wuersching, Markus Schnappinger, Alexander Pretschner, Maria Graber, René Dammer, Silke Reimer:
Build System Aware Multi-language Regression Test Selection in Continuous Integration. ICSE (SEIP) 2022: 87-96 - [c6]Markus Schnappinger, Simon Zachau, Arnaud Fietzke, Alexander Pretschner:
A Preliminary Study on Using Text- and Image-Based Machine Learning to Predict Software Maintainability. SWQD 2022: 41-60 - 2021
- [c5]Markus Schnappinger, Arnaud Fietzke, Alexander Pretschner:
Human-level Ordinal Maintainability Prediction Based on Static Code Metrics. EASE 2021: 160-169 - [c4]Markus Schnappinger, Jonathan Streit
:
Efficient Platform Migration of a Mainframe Legacy System Using Custom Transpilation. ICSME 2021: 545-554 - 2020
- [c3]Markus Schnappinger, Arnaud Fietzke, Alexander Pretschner:
Defining a Software Maintainability Dataset: Collecting, Aggregating and Analysing Expert Evaluations of Software Maintainability. ICSME 2020: 278-289
2010 – 2019
- 2019
- [c2]Markus Schnappinger, Mohd Hafeez Osman, Alexander Pretschner, Arnaud Fietzke:
Learning a classifier for prediction of maintainability based on static analysis tools. ICPC 2019: 243-248 - 2018
- [c1]Markus Schnappinger, Mohd Hafeez Osman, Alexander Pretschner, Markus Pizka, Arnaud Fietzke:
Software quality assessment in practice: a hypothesis-driven framework. ESEM 2018: 40:1-40:6
Coauthor Index
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