Feature Maps: A Comprehensible Software Representation for Design Pattern Detection
Presents Feature Maps that are a human-readable representation of software. This work uses Feature Maps to detect Design Patterns via supervised machine learning methods (CNNs and RFs).*
Authors: H. Thaller, L. Linsbauer, and A. Egyed
Published in: 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER), Hangzhou, China, 2019, pp. 207-217
DOI: 10.1109/SANER.2019.8667978
PDF arXiv Presentation
Benefits and Drawbacks of Representing and Analyzing Source Code and Software Engineering Artifacts with Graph Databases
Distills benefits and drawbacks of graph databases for static code analysis.
Authors: R. Ramler, G. Buchgeher, C. Klammer, M. Pfeiffer, C. Salomon, H. Thaller, and L. Linsbauer
Published in: Software Quality: The Complexity and Challenges of Software Engineering and Software Quality in the Cloud, vol. 338, D. Winkler, S. Biffl, and J. Bergsmann, Eds. Cham: Springer International Publishing, 2019, pp. 125–148.
DOI: 10.1007/978-3-030-05767-1_9
Probabilistic Software Modeling
Describes Probabilistic Software Modeling (PSM) a data-driven paradigm for structural and behavioral software comprehension. PSM enables applications range from visualization of states, inferential queries, test case generation, and anomaly detection up to the stochastic execution of the modeled system.
Author: H. Thaller
Accepted at: 2018 ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA), Amsterdam, 2018, ECOOP and ISSTA Doc Symposium
PDF arXiv Poster Presentation
Exploring Code Clones in Programmable Logic Controller Software
Explores and characterizes clones in PLC software.
Authors: H. Thaller, R. Ramler, J. Pichler, and A. Egyed
Published in: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Limassol, 2017, pp. 1-8.
DOI: 10.1109/ETFA.2017.8247574
PDF arXiv
Subliminal Visual Information to Enhance Driver Awareness and Induce Behavior Change
Measures the effect of *subliminal cues during driving.*
Authors: A. Riener and H. Thaller
Published in: AutomotiveUI ‘14 Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Pages 1-9
DOI: 10.1145/2667317.2667328


Towards Deep Learning Driven Design Pattern Detection
Explores the capabilities of deep learning methods in the context of software design pattern detection.
Author: H. Thaller
Supervisor: A. Egyed
Driver Performance Manipulation via Visual Subliminal Cues
Explores the capabilities of visual subliminal messages to influence the driving behavior in order to mitigate rear-end accidents.
Author: H. Thaller
Supervisor: A. Riener


Cluster Analysis for Multivariate Application Performance Management Issues
Clusters multivariate performance problems in application performance management systems via unsupervised machine learning to reduce the resulting alarm floods.
Author: V. Precup
Supervisor: A. Egyed and H. Thaller