Reliable validation of autonomous driving cyber-physical systems requires testing not only nominal driving behavior but also failures arising from faults in sensing and embedded computation. Physical...
arXiv:2606. 19725v1 Announce Type: cross Abstract: Validating changes in low-level C firmware is expensive because unit tests (UTs) are fragile under strict build constraints, where missing headers, unresolved symbols, and dependency mismatches frequently prevent compilation and linking.
By Ma Toan Bach, Yuchi Zheng, Haingo Razafindranto, Tanvir Alam, Aric Leather, Ranveer Sandhu, Jitesh Arora
arXiv:2607. 16388v1 Announce Type: cross Abstract: Large-scale AI datacenter platforms comprise thousands of heterogeneous hardware components whose validation requires comprehensive fault injection test plans.
By Mohammed-Khalil Ghali, Saurabh Kulkarni, Prathamesh Kulkarni, Rohan Kulkarni, Sangwon Yoon, Daehan Won
arXiv:2503. 08936v3 Announce Type: replace-cross Abstract: Scenario-based testing with driving simulators is extensively used to identify failing conditions of automated driving assistance systems (ADAS).
By Lev Sorokin, Matteo Biagiola, Andrea Stocco
The paper introduces NeuroAssertion, a coverage-driven framework for generating RTL assertions that integrates formal trace generation, syntax-guided synthesis, and a two-step large language model (LLM) refinement process. It transforms hard-to-reach control-flow conditions into formal reachability goals, uses model checking to produce diverse traces, and mines initial assertions via SyGuS. The refinement stage employs one LLM to propose candidate assertions for uncovered regions and a second LLM to generate repair grammars that guide symbolic synthesis, resulting in roughly twice as many assertions and higher mutation coverage compared to traditional methods.
By Zhiyuan Yan, Ziyue Zheng, Hongce Zhang
arXiv:2604. 02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data.
By Jiyong Kwon, Ujin Jeon, Sooji Lee, Guang Lin