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Context: AI code generators are revolutionizing code writing and software development, but their training on large datasets, including potentially untrusted source code, raises security concerns. Furthermore, these generators can produce incomplete code snippets that are challenging to evaluate using current solutions. Objective: This research work introduces DeVAIC (Detection of Vulnerabilities in AI-generated Code), a tool to evaluate the security of AI-generated Python code, which overcomes the challenge of examining incomplete code. Method: We followed a methodological approach that involved gathering vulnerable samples, extracting implementation patterns, and creating regular expressions to develop the proposed tool. The implementation of DeVAIC includes a set of detection rules based on regular expressions that cover 35 Common Weakness Enumerations (CWEs) falling under the OWASP Top 10 vulnerability categories. Results: We utilized four popular AI models to generate Python code, which we then used as a foundation to evaluate the effectiveness of our tool. DeVAIC demonstrated a statistically significant difference in its ability to detect security vulnerabilities compared to the state-of-the-art solutions, showing an F1 Score and Accuracy of 94% while maintaining a low computational cost of 0.14 seconds per code snippet, on average. Conclusions: The proposed tool provides a lightweight and efficient solution for vulnerability detection even on incomplete code.

The thesis deals with the study of the asymptotic behaviour of the solutions of nonautonomous Lotka-Volterra models, around the biologically meaningful equilibrium of the classical autonomous system. The Liapunov Direct Method for nonautonomous ODE is used in order to find conditions assuring the (local) asymptotic stability of the equilibrium. When these conditions hold, the system can not admit cycles.

The thesis deals with the study of the asymptotic behaviour of the solutions of nonautonomous Lotka-Volterra models, around the biologically meaningful equilibrium of the classical autonomous system. The Liapunov Direct Method for nonautonomous ODE is used in order to find conditions assuring the (local) asymptotic stability of the equilibrium. When these conditions hold, the system can not admit cycles.

The thesis deals with the study of the asymptotic behaviour of the solutions of nonautonomous Lotka-Volterra models, around the biologically meaningful equilibrium of the classical autonomous system. The Liapunov Direct Method for nonautonomous ODE is used in order to find conditions assuring the (local) asymptotic stability of the equilibrium. When these conditions hold, the system can not admit cycles.