Projects
Projects, Articles, and Theses
Projects
Projects, Articles, and Theses
Projects
Testing Platform for Game Development
A testing service that allow Unit-Test like tests for game studios and games in video space, i.e., while the game is controlled and naturally played by testers.| Type | Commercial Project |
|---|---|
| Timeline | 2022 - current |
| Duration | running |
| Responsibilities | Machine Learning Engineer - responsible for the initial design and development of the machine learning platform and structuring of the data flows. |
| Machine Learning Researcher - responsible for the initial problems specification and solution design as further ideation for downstream products in the context of Game Dev QA. |
Bid Management System for Google Shopping
A bid management system is often used in the context of online marketing where multiple advertisers compete for a limited advertisement space on the internet. The project solved a multi-faceted regression problem intersecting feature engineering, regression, time-series analysis, incrementality testing, and large-scale daily ML Ops of the system. A particular challenge is given in the broad spectrum of data encountered in the commercial system that actively influences the data it will consume in the future. Another challenge is given in the daily operation and quality assurance of the produced predictions. Commercial systems need multiple quality gates that slowly increase the impact radius of its effect while continuously monitoring machine learning and business performance.| Type | Commercial Project |
|---|---|
| Timeline | 2020 - 2022 |
| Duration | 2 years |
| Responsibilities | Product Data Scientist - responsible for the strategic innovative and technical soundness of the system from a data science perspective. |
| Data Science Software Architect - responsible for the architectural evolution of the ML pipeline that is integrated into a larger set of systems. |
Probabilistic Software Modeling
The projects devised a new modeling paradigm that transforms a program into a probabilistic model. The resulting model can be used for analytical and generative applications in software engineering. For example, it can detect semantic clones (e.g., iterative and recursive implementation of factorial) within programs. Another example would be the localization of faults based on semantic/behavioral divergences. Gradient, an implementation of Probabilistic Software Modeling, uses classical static code analysis, high-performance distributed computing for the runtime monitoring, and state-of-art neural density estimation for building the probabilistic model.| Type | Research Project |
|---|---|
| Timeline | 2016 - 2021 |
| Duration | 5 years |
| Responsibilities | Principal Researcher - developing the theoretical concepts defining its strategic roadmap |
| Designer and Developer - designing and architecting the theoretical concepts into workable practical components |
Design Pattern Detection via Feature Maps
The project created an innovative representation of the programs called feature maps. Features maps are images of the structural properties of a program and can be used to detect design patterns. The system uses static code analysis and a wide range of graph transformations to extract crucial program properties. These properties are then projected into matrices that represent an image of the program.| Type | Research Project |
|---|---|
| Timeline | 2015 - 2016 |
| Duration | 1 year |
| Responsibilities | Principal Researcher - developing the theoretical concepts defining its strategic roadmap |
| Designer and Developer - designing and architecting the theoretical concepts into workable practical components |
Papers
Semantic Clone Detection via Probabilistic Software Modeling
Presents and evaluates semantic clone detection using probabilistic software modeling.| Authors | H. Thaller, L. Linsbauer, and A. Egyed |
|---|---|
| Published In | Johnsen, E.B., Wimmer, M. (eds) Fundamental Approaches to Software Engineering. FASE 2022. Lecture Notes in Computer Science, vol 13241. Springer, Cham. |
| DOI | 10.1007/978-3-030-99429-7_16 |
| ISBN | 978-3-030-99429-7 |
| Media |
Towards Semantic Clone Detection via Probabilistic Software Modeling
Outlines the use of Probabilistic Software Modeling to detect semantically equivalent code elements.| Authors | H. Thaller, L. Linsbauer, and A. Egyed |
|---|---|
| Published In | 2020 IEEE 14th International Workshop on Software Clones (IWSC), London, ON, Canada, 2020, pp. 64--69 |
| DOI | 10.1109/IWSC50091.2020.9047635 |
| Media |
Towards Fault Localization via Probabilistic Software Modeling
Outlines the use of Probabilistic Software Modeling to localize faults and their impact across program elements.| Authors | H. Thaller, L. Linsbauer, A. Egyed, and S. Fischer |
|---|---|
| Published In | 2020 IEEE 3rd International Workshop on Validation, Analysis, and Evolution of Software Tests (VST), London, ON, Canada, 2020, pp. 24--27 |
| DOI | 10.1109/VST50071.2020.9051635 |
| Media |
An Empirical Evaluation for Object Initialization of Member Variables in Unit Testing
Explores the viability of unintrusive object initialization for test case generation.| Authors | S. Fischer, E.N. Hasling, M. Zimmermann, and H. Thaller |
|---|---|
| Published In | 2020 IEEE 3rd International Workshop on Validation, Analysis, and Evolution of Software Tests (VST), London, ON, Canada, 2020, pp. 8--11 |
| DOI | 10.1109/VST50071.2020.9051634 |
| Media |
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 |
| Media |
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 |
| Media |
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 |
| Media |
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 |
| Media |
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 |
| Media |