In large programming classes, it takes a significant effort for teachers to evaluate exercises and provide detailed feedback. Test cases are not sufficient to assess systems programming exercises, since concurrency and resource management bugs are difficult to reproduce. This paper presents an experience report on the automatic evaluation of system programming exercises using static analysis. We present the design of systems programming assignments, and of static analysis rules that are tailored for each assignment to make them detailed and accurate. Our evaluation shows that static analysis can identify a significant number of erroneous submissions missed by test cases.

I am Associate Professor in Computer Engineering at the Federico II University of Naples, Italy. My research interests are in the field of software security and dependability. The main recurring theme of my research activity is the experimental injection of faults, attacks, and stressful conditions.

My research topics include: fuzzing and static analysis; red teaming, adversary emulation, cyber ranges; cyber threat intelligence; machine learning techniques for security; fault injection, robustness testing, dependability benchmarking; software aging and rejuvenation; applications in operating systems and in cloud, mobile, embedded, and virtualization technologies.

In 2022, I received the DSN Rising Star in Dependability Award from the IEEE Technical Committee on Dependable Computing and Fault Tolerance (TCFT) and the IFIP Working Group 10.4 on Dependable Computing and Fault Tolerance, for research achievements within 10 years after PhD graduation.