No abstract

TLM (Transaction-Level Modeling) was introduced to cope with the increasing complexity of Systems-on-Chip designs by raising the modeling level. Currently, TLM is primarily used for system-level functional testing and simulation using the SystemC C++ API widely accepted in industry. Nevertheless, TLM requires a careful handling of asynchronous concurrency. In this paper, we give a semantics to TLM models written in SystemC via a translation into the process algebra LOTOS, enabling the verification of the models with the CADP toolbox dedicated to asynchronous systems. Contrary to other works on formal verification of TLM models written in SystemC, our approach targets fully asynchronous TLM without the restrictions imposed by the Sys-temC simulation semantics. We argue that this approach leads to more dependable models.

No abstract

Static value analysis is a classical approach for verifying programs with floating-point computations. Value analysis mainly relies on abstract interpretation and over-approximates the possible values of program variables. State-of-the-art tools may however compute over-approximations that can be rather coarse for some very usual program expressions. In this paper, we show that constraint solvers can significantly refine approximations computed with abstract interpretation tools. More precisely, we introduce a hybrid approach combining abstract interpretation and constraint programming techniques in a single static and automatic analysis. This hybrid approach benefits of the strong points of abstract interpretation and constraint programming techniques, and thus, it is more effective than static analysers and constraint solvers, when used separately. We compared the efficiency of the system we developed-named RAICP-with state-of-the-art static analyzers: RAICP produces substantially more precise approximations and is able to check program properties on both academic and industrial benchmarks.Keywords Program verification · Floating-point computation · Constraint solving over floating-point numbers · Constraint solving over real number intervals · Abstract interpretation-based approximation This work was partially supported by ANR VACSIM (ANR-11-INSE-0004), ANR AEOLUS (ANR-10-SEGI-0013), and OSEO ISI PAJERO projects.

International audienceAbstract interpretation based value analysis is a classical approach for verifying programs with floating-point computations. However, state-of-the-art tools compute an over-approximation of the variable values that can be very coarse. In this paper, we show that constraint solvers can significantly refine the approximations computed with abstract interpretation tools. We introduce a hybrid approach that combines abstract interpretation and constraint programming techniques in a single static and automatic analysis. RAICP, the system we developed is substantially more precise than FLUCTUAT, a state-of-the-art static analyser. Moreover, it could eliminate 13 false alarms generated by FLUCTUAT on a standard set of benchmarks