Feature Maps: A Comprehensible Software Representation for Design Pattern Detection

Feature Maps are a human-readable representation of software that are useful, e.g., to detect design patterns via supervised machine learning (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

Benefits and Drawbacks of Representing and Analyzing Source Code and Software Engineering Artifacts with Graph Databases

Insights and experiences of five cases using graph databases in static code analysis settings.
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

A modeling approach that analyzes structure and behavior of applications and reconstructs it using a network of generative probabilistic models.
Authors H. Thaller
Published In 2018 ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA), Amsterdam, 2018, ECOOP and ISSTA Doc Symposium

Exploring Code Clones in Programmable Logic Controller Software

Code clones exist in PLC software, and the development can benefit from better tooling.
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

Subliminal Visual Information to Enhance Driver Awareness and Induce Behavior Change

Subliminal visual information has enormous potential to reduce the cognitive load of drivers, but it is too weak to stress critical behavior change.
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

Convolutional Neural Networks can detect design patterns in a volumetric abstraction of the source code in even the most imbalanced settings.
Authors H. Thaller
Supervisor A. Egyed

Driver Performance Manipulation via Visual Subliminal Cues

Subliminal cues are unintrusive, but a weak mechanism to feed information to drivers, e.g., to mitigate rear-end accidents.
Authors H. Thaller
Supervisor A. Riener


Cluster Analysis for Multivariate Application Performance Management Issues

Clustering can control alarm floods in application performance management systems improving reporting and analysis of large scale systems.
Authors V. Precup
Supervisor A. Egyed and H. Thaller