From Discrete Duration Calculus to Symbolic Automata

The goal of this paper is to translate (fragments of) the quantified discrete duration calculus QDDC, proposed by P. Pandya, into symbolic acceptors with counters. Acceptors are written in the synchronous programming language Lustre, in order to allow available symbolic verification tools (model-checkers, abstract interpreters) to be applied to properties expressed in QDDC. We show that important constructs of QDDC need non-deterministic acceptors, in order to be translated with a bounded number of counters, and an expressive fragment of the logic is identified and translated. Then, we consider a more restricted fragment, which only needs deterministic acceptors.     

Purification of cobra venom factor from phospholipase A contaminant

It has been demonstrated that cobra venom factor prepared by the usual combination of ion exchange chromatography and sephadex gel filtration is contaminated by substantial amounts of a 'heavy' phospholipase A. The two activities may be separated by isoelectric focusing. Cobra venom factor focuses at pH between 5-75 and 6-75 whereas the phospholipase is all found at pH below 7-75. In certain test systems, particularly in vitro, and particularly where albumin concentrations are low, the contaminating phospholipase may produce effects that have been attributed to complement activation.     

Verification of Real-Time Systems using Linear Relation Analysis

Linear Relation Analysis [11] is an abstract interpretation devoted to the automatic discovery of invariant linear inequalities among numerical variables of a program. In this paper, we apply such an analysis to the verification of quantitative time properties of two kinds of systems: synchronous programs and linear hybrid systems.     

The synchronous languages 12 years later

Twelve years ago, Proceedings of the IEEE devoted a special section to the synchronous languages. This paper discusses the improvements, difficulties, and successes that have occured with the synchronous languages since then. Today, synchronous languages have been established as a technology of choice for modeling, specifying, validating, and implementing real-time embedded applications. The paradigm of synchrony has emerged as an engineer-friendly design method based on mathematically sound tools.     

Counter-example generation in symbolic abstract model-checking

The boundaries of model-checking have been extended through the use of abstraction. These techniques are conservative, in the following sense: when the verification succeeds, the verified property is guaranteed to hold; but when it fails, it may result either from the non satisfaction of the property, or from a too rough abstraction. In case of failure, it is, in general, undecidable whether an abstract trace corresponding to a counter-example has any concrete counterparts. For debugging purposes, one usually desires to go further than giving a “yes/no” answer (actually, a “yes/don’t know” answer!), and look for such concrete counter-examples. We propose a solution in which we apply standard test-pattern generation technology to search for concrete instances of abstract traces.     

Improving the results of program analysis by abstract interpretation beyond the decreasing sequence

The classical method for program analysis by abstract interpretation consists in computing first an increasing sequence using an extrapolation operation, called widening, to correctly approximate the limit of the sequence. Then, this approximation is improved by computing a decreasing sequence without widening, the terms of which are all correct, more and more precise approximations. It is generally admitted that, when the decreasing sequence reaches a fixpoint, it cannot be improved further. As a consequence, most efforts for improving the precision of an analysis have been devoted to improving the limit of the increasing sequence. In a previous paper, we proposed a method to improve a fixpoint after its computation. This method consists in computing from the obtained solution a new starting value from which increasing and decreasing sequences are computed again. The new starting value is obtained by projecting the solution onto well-chosen components. The present paper extends and improves the previous paper: the method is discussed in view of some example programs for which it fails. A new method is proposed to choose the restarting value: the restarting value is no longer a simple projection, but is built by gathering and combining information backward the widening nodes in the basic solution. Experiments show that the new method properly solves all our examples, and improves significantly the results obtained on a classical benchmark.