Alessio Foggia

Domenico Cotroneo, Alessio Foggia, Cristina Improta et al.

Evaluating the correctness of code generated by AI is a challenging open problem. In this paper, we propose a fully automated method, named ACCA, to evaluate the correctness of AI-generated code for security purposes. The method uses symbolic execution to assess whether the AI-generated code behaves as a reference implementation. We use ACCA to assess four state-of-the-art models trained to generate security-oriented assembly code and compare the results of the evaluation with different baseline solutions, including output similarity metrics, widely used in the field, and the well-known ChatGPT, the AI-powered language model developed by OpenAI. Our experiments show that our method outperforms the baseline solutions and assesses the correctness of the AI-generated code similar to the human-based evaluation, which is considered the ground truth for the assessment in the field. Moreover, ACCA has a very strong correlation with the human evaluation (Pearson's correlation coefficient r=0.84 on average). Finally, since it is a fully automated solution that does not require any human intervention, the proposed method performs the assessment of every code snippet in ~0.17s on average, which is definitely lower than the average time required by human analysts to manually inspect the code, based on our experience.

Carmine Cesarano, Alessio Foggia, Roberto Natella

The convergence of Passive Optical Networks (PONs) and edge computing creates new opportunities: Optical Line Terminals (OLTs) and Optical Network Terminals (ONTs) can be repurposed as low-latency edge compute nodes for offloading workloads. However, exploring such design options early in the development cycle is costly and time-consuming, as prototyping requires specialized hardware and realistic traffic conditions. Simulation becomes essential, yet current tools are unable to accurately model this emerging class of systems. To address these gaps, we introduce GenioSim, a simulation platform for hierarchical PON-enabled edge infrastructures. It models OLTs and ONTs with realistic PON behavior, supports hybrid container- and VM-based virtualization, and provides multiple service and execution models. These capabilities enable the evaluation of resource management policies under complex, heterogeneous conditions. We present experiments in the context of use cases of industrial relevance, to show GenioSim can provide insights for capacity planning and for the choice of policies for container placement and task offloading in PON-enabled edge infrastructures.