STILL (Software Testing IntelLigent Lab)

STILL is a research lab located in the Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague dedicated to research in software testing and search based software engineering. The overall aim of the lab is to develop new tools and strategies for testing complex software systems in efficient and automated way. The lab also aims to help industrial partners for developing cost-effective and advanced testing techniques for real-world systems.

Projects run under the Red Hat lab or in Collaboration. 
1. CIT for Avocado Framework
The project aims to design and implement a new plugin to the open-source Avocado framework which allows generating much more effcieneng test data. The new plugin will add the capability of Combinatorial Interaction Testing (CIT) to the framework by decreasing the number of test cases effectively. For a long running plan, the project also aims to add the constraint solving algorithms to the framework to generate constrained test cases.
2. Quality assurance for IoT
The main goal of the project is to design, implement and verify a framework for quality assurance of products based on the Internet of Things (IoT) concept. The aim of the framework is to help individual IoT projects to establish an efficient testing and verification strategy of the infrastructure. Efficient quality assurance decreases project and product risks as well as the overhead and economic losses caused by inefficient and reactive way of testing. The proposed framework will base on a model of the IoT infrastructure, composed of methodological and technical parts. The design of the framework aims to be compliant with continuous integration approach emphasizing automation of testing and the quality assurance process.
3. Test generation using code analysis.
The goal of this project is to create a method for generating test cases and determining its effectiveness. The method is created based on an analysis of a given program consisting of determining the effect which each program argument has on the overall volume of source code executed. To achieve this, an application is created that can analyze the programs code coverage for various permutations of arguments, generate test cases. The application can also determine the tests effectiveness, by using them to detect seeded faults.