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Abstract. We propose a symbolic algorithm to accurately predict atomicity violations by analyzing a concrete execution trace of a concurrent program. We use both the execution trace and the program source code to construct a symbolic predictive model, which captures a large set of alternative interleavings of the events of the given trace. We use precise symbolic reasoning with a satisfiability modulo theory (SMT) solver to check the feasible interleavings for atomicity violations. Our algorithm differs from the existing methods in that all reported atomicity violations can appear in the actual program execution; and at the same time the feasible interleavings analyzed by our model are significantly more than other predictive models that guarantee the absence of false alarms.

Abstract-With the success of formal verification techniques like equivalence checking and model checking for hardware designs, there has been growing interest in applying such techniques for formal analysis and automatic verification of software programs. This paper provides a brief tutorial on model checking of C programs. The essential approach is to model the semantics of C programs in the form of finite state systems by using suitable abstractions. The use of abstractions is key, both for modeling programs as finite state systems and for reducing the model sizes in order to manage verification complexity. We provide illustrative details of a verification platform called F-SOFT, which provides a range of abstractions for modeling software, and uses customized SAT-based and BDD-based model checking techniques targeted for software.

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In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.

Abstract. We present an efficient method for modeling multi-threaded concurrent systems with shared variables and locks in Bounded Model Checking (BMC), and use it to improve the detection of safety properties such as data races. Previous approaches based on synchronous modeling of interleaving semantics do not scale up well due to the inherent asynchronism in those models. Instead, in our approach, we first create independent (uncoupled) models for each individual thread in the system, then explicitly add additional synchronization variables and constraints, incrementally, and only where such synchronization is needed to guarantee the (chosen) concurrency semantics (based on sequential consistency). We describe our modeling in detail and report verification results to demonstrate the efficacy of our approach on a complex case study.

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