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Automated Quality Assurance


Automated Quality Assurance Automata Learning

"Automated Quality Assurance" is the assurance that software components are free of errors through automated processes. This goes far beyond classical methods such as unit tests and includes the formal specification of desired system behavior, the use of these specifications to automatically verify freedom from defects and the development of tools that integrate these steps into the normal life cycle of software.


Real-world systems often are developed with little or no specifications. By using Automata Learning, we reconstruct a system's behavior as an automaton based on the its visible behavior. These models can then be checked against requirements, utilized as a proxy for the component or employed to discover aberrent behavior. Our researched is focused on passive learning techniques that learn from observation only and Register Automata that can model storage over infinite data domains.

Automata Learning

Precise IT-Security Analysis Autonomous Systems
We combine dynamic symbolic execution with dynamic taint analysis for analyzing the IT-Security of Java binaries. The dynamic symbolic execution is used as a driver for exploring the complete system state. Dynamic taint analysis allows precise guards along the paths in the system state, that are explored by the driver. Whenever a taint violation is found, the dynamic symbolic execution engine can produce a counter example to get there. This is the first step towards automated exploit generation and automated security analysis.


Autonomous systems, e.g. autonomous vehicles, in real environment are subject to high safety requirements, because they have to operate side by side with humans safely. However, the complexities of real environments make it difficult to verify the safety of these systems. It is especially important not to verify the safety in presence of faults but also the correctness and safety of their nominal system behavior. Aqua researches novel development and verification approaches to verify the safety of such autonomous systems at design time and runtime.


Industrial Apllications for Type-Based Synthesis Management of IT - innovation 
Synthesis, i.e. the automatic composition of models, has been used in computer science for many years to automatically generate software product lines. We use these approaches to make the modeling and planning of numerous industrial models and processes automatable. In this context, the research group deals with the automatic generation of BIM models and factory planning workflows.


 Today it becomes increasingly difficult for organizations to survive in the vastly changing environment of global markets, newly marketed digital technologies and the boom of novel start-ups and business models. IT is the enabler and nervous system of this change and future. To make the most of it and strengthen the industry, computer scientists need to get prepared for this strategic responsibility. Thus, we focus on and empower the interlocking of IT in particular with regard to software engineering and innovation in the context of strategic directions and business models.




Prof. Dr. Falk Howar

Wissenschaftliche Mitarbeiterinnen und Mitarbeiter

Simon Dierl, M.Sc.
Dr. Malte Mauritz
Malte Mues, M.Sc.
Dr. Stefan Naujokat
Till Schallau, M.Sc.
Dr.-Ing. Jan Winkels

Ehemalige Wissenschaftliche Mitarbeiterinnen und Mitarbeiter

Barbara Steffen, M.Sc.




Prof. Dr. Falk Howar

Tel.: 0231 755-7945

Sekretariat Lehrstuhl 14

Ute Joschko 

(+49)231 755-7953

(+49)231 755-7936


Technische Universität Dortmund

Fakultät für Informatik 
LS 14 - Software Engineering

Otto-Hahn-Str. 12
44227 Dortmund