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Different Machine Learning techniques to detect software vulnerabilities have emerged in scientific and industrial scenarios. Different actors in these scenarios aim to develop algorithms for predicting security threats without requiring human intervention. However, these algorithms require data-driven engines based on the processing of huge amounts of data, known as datasets. This paper introduces the SonarCloud Vulnerable Code Prospector for C (SVCP4C). This tool aims to collect vulnerable source code from open source repositories linked to SonarCloud, an online tool that performs static analysis and tags the potentially vulnerable code. The tool provides a set of tagged files suitable for extracting features and creating training datasets for Machine Learning algorithms. This study presents a descriptive analysis of these files and overviews current status of C vulnerabilities, specifically buffer overflow, in the reviewed public repositories.

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Using 3D computer simulations for training surgeons is not new. Using e-learning for improving students knowledge acquisition is not new. What we propose is to use 3D computer simulations in such a versatile way that those simulations could act as learning objects designed directly by those who own the experience we want to be transmitted. In order to achieve this goal, it is necessary to create a model in charge of communications between the learning objects and the simulation. This model ensures that, on the one hand, the simulation offers an interface to the learning process stable enough not to be affected by every small change. On the other hand, the model also ensures that the simulation offers an interface complete enough for adopting any change in the learning process. The key to solve this contradiction is to take the behavior of the simulation objects out of their control leaving in them just their very basic behavior. This paper presents the problem and the design proposed to solve it in a more detailed way.

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