Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Security and Verification in Computing
Abstract
This research presents a comparative analysis of the safety and security characteristics of C and Rust on Raspberry Pi systems. Using a Raspberry Pi 3 Model B+ running a 64-bit Linux environment, the study evaluates how both programming languages behave under controlled test cases involving common software vulnerabilities and runtime failures. The experiments examine buffer overflow, use-after-free and dangling pointers, integer overflow, format-string vulnerabilities, stack exhaustion, heap allocation, null pointer handling, and thread panic behavior. The study focuses on the differences between C's manual memory management model and Rust's ownership, borrowing, type-safety, and runtime safety mechanisms. Experimental results demonstrate how several vulnerability classes can compile and reach execution in C, while safe Rust prevents or detects many comparable unsafe operations through compile-time checks, explicit APIs, or runtime panics. The research also discusses the limitations of the experimental environment and the implications of adopting Rust for memory-safe systems programming on ARM-based embedded and edge-computing platforms.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The perception of the impact of agile methods is predominantly positive, and several challenge areas were discovered, but based on this study, agile methods are here to stay.
M. Laanti, O. Salo, P. Abrahamsson· Information and Software Tec...· 260 citations· ⚡20
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MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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