Abstract. The goal of the SLAM project is to check whether or not a program obeys "API usage rules " that specif[y what it means to be a good client of an API. The SLAM toolkit statically analyzes a C program to determine whether or not it violates given usage rules. The toolkit has two

of programs written in an industrial programming language such as C, and its implementation in a tool-- C2bp. The C2bp tool is part of the SLAM toolkit, which uses a combination of predicate abstraction, model checking, symbolic reasoning, and iterative refinement to statically check temporal safety

. The process is realized in the SLAM toolkit, which consists of a model checker, predicate abstraction tool and predicate discovery tool. We have applied the SLAM toolkit to a number of Windows NT device drivers to validate critical safety properties such as correct locking behavior. We have found

The Slam toolkit demonstrates that predicate abstraction enables automated verification of real world Windows device drivers. Our predicate abstraction-based tool DDVerify enables the automated verification of Linux device drivers and provides an accurate model of the relevant parts of the kernel

This report addresses the formulation and the implementation of a full 6D (3D positions plus 3D attitude angles) solution to Simultaneous Localization and Mapping (SLAM) using a range and bearing sensor. It will be not assumed here that the sensor coordinate reference system coincide

The application of automated theorem proving to program verification is increasingly being used in real-world situations, most of which are concerned with proving safety properties of code. For example, the Berkeley Lazy Abstraction Software Verification Tool [4] (Blast), and Microsoft r○’s SLAM

applications containing the procedure. Polymorphism also enables us to handle programs that need to be analyzed in an open environment, for all possible callers. We have proved that our algorithm is sound and have implemented it in the C2BP tool as part of the SLAM software model checking toolkit.

of the computational fluid dynamics (CFD) methods are based on eulerian representation and use grids to describe the geometry of the simulated domain. These differences make the MPS method easier to analyze highly nonlinear phenomena as free surface with wave breaking, sloshing, slamming, etc. In previously published

First, I would like to thank my advisors Prof. Gal A. Kaminka and Dr. David Sarne for their excellent guidance and constant support in the past two years. I learned quite a bit about scientific research, all thanks to their great instruction. Working with them was a real pleasure, not only on the professional level, but on the personal level as well. Thanks to my colleagues and friends from the Bar-Ilan University in general, and MAVERICK lab in particular, for the great time we had together. Special thanks to Gabriella Melamed, MAVERICK lab manager, for assisting with administra-tive issues, and in particular for helping with the arrangements for the experiment. I also wish to thank Tal-Oron Gilad from Ben-Gurion University, for providing us with a He-brew translation of the NASA-TLX questionnaire. Thanks to my dear family for their support and encouragement, and for enabling me to com-plete this work. Thanks to my girlfriend for her patience and understanding. Finally, I would like to thank my grandfather, who never ceased to encourage me throughout this journey.