Although paramount for a safe society, software testing is often perceived as a boring, unrewarding activity. Moreover, the assessment of those few who wish to enter the field, is not always fast, reliable and unbiased. DBugIT is an interactive online tool, recently developed at the Vrije Universiteit (VU) that solves these problems by engaging users in an exciting bug-hunting game, replacing the traditional job interviews and pen-and-paper exams. After receiving high appreciation inside VU, DBugIT is ready to grow and become the No.1 game for an exciting, unbiased software testing learning and recruiting experience.

Is the amount of malicious software on smartphones growing? Sadly, we lack the tools to analyze smartphone applications in sufficient detail to decide whether or not software is malicious or not, and if so, what it does exactly. In this project an environment will be developed to gather and analyze malware on smartphones and new techniques to analyze the software in detail, using both static and dynamic analysis. Building on previous work of our groups (like TraceDroid and Andrubis), we plan to make our research efforts available to external parties so that they can submit samples for analysis also.

Networked embedded devices like routers, switches, firewalls and sensors are part of our critical infrastructure. These devices are often assembled and programmed overseas -- beyond our control -- and placed within our trusted networks or even used for military applications. But can we really trust them? There have been several incidents where backdoors have been found in the firmware of these devices. Such a backdoor allows an adversary to gain access to the device. The OpenSesame project addresses this problem by developing novel automated techniques to test the software and firmware of embedded devices for the presence of such backdoors.

Currently, there is an explosion of sensors from smart phones and the Internet of Things (IoT). Processing the data from this ever-growing number of sensors is challenging and requires massive computing resources. Processing large-scale sensor data is a new scenario for High Performance Computing (HPC), because it entails operating on highly dynamic, widely distributed data and it often needs fast response times. Most existing HPC solutions for sensors send all data to a shared Cloud data center and process it there. Unfortunately, this approach is unsuitable for applications that need fast responses or that have privacy issues, and also the Internet bandwidth required by the huge number of sensors will escalate. Therefore, there is an urgent need to have a distributed approach that can also do computations on the sensors or in nearby (cloudlet, edge, fog) resources. The distribution leads to a complex, dynamic, online resource management and scheduling (RM&S) problem, where many sensing-application components must be transparently managed and scheduled subject to restrictions (like performance, privacy, energy consumption). Component migration (offloading) is the key challenge here. Another major problem is that current programming paradigms are insufficient to address the complexity of future IoT applications running on a diversity of distributed hardware. To address these problems, the OffSense project will develop a generalized computation offloading model. The project will introduce several key innovations, including, a reference RM&S architecture, a portfolio technique to dynamically manage RM&S solutions, a declarative language for distributed sensor applications, and a methodology for experiments with distributed sensing. Many domains will benefit from our project, including smart cities, safety, smart buildings, and smart industry.