The success of model checking is largely based on its ability toe-ciently locate errors in software designs. If an error is found, a model checker produces a trail that shows how the error state can be reached, which greatly facilitates debugging. However, while current modelcheckers nd error states e ciently, the counterexamples are often unnecessarily lengthy, which hampers error explanation. This is due to the use of \naive " search algorithms in the state space exploration. In this paper we present approaches to the use of heuristic search algorithms in explicit-state model checking. We present the class of A* directed search algorithms and propose heuristics together with bitstate compression techniques for the search ofsafetyproperty violations. We achieve great reductions in the length of the error trails, and in some instances render problems analyzable by exploring a much smaller number of states than standard depth- rst search. We then suggest an improvement of the nested depth- rst search algorithm and show how it can be used together with A * to improve the search for liveness property violations. Our approach to directed explicit-state model checking has been implemented in a tool set called HSF-SPIN. We provide experimental results from the protocol validation domain using HSF-SPIN.

Directed model checking has proved itself to be a useful technique in reducing the state space of the problem graph. But still, its potential is limited by the available memory. This problem can be circumvented by the use of secondary storage devices to store the state space. This paper discusses directed best-first search to enhance error detection capabilities of model checkers like SPIN by using a streamed access to secondary storage. We explain, how to extend SPIN to allow external state access, and how to adapt heuristic search algorithms to ease error detection for this case. We call our derivate IO-HSF-SPIN. In the theoretical part of the paper, we extend the heuristic-based external searching algorithm to general weighted and directed graphs. We conduct experiments with some challenging protocols in Promela syntax like GIOP and dining philosophers and have succeeded in solving some hard instances externally.

Abstract. We describe an improvement of the partial-order reduction algorithm for breadth-first search which was introduced in Spin version 4.0. Our improvement is based on the algorithm by Alur et al. for symbolic state model checking for local safety properties [1]. The crux of the improvement is an optimization in the context of explicit state model checking of the condition that prevents action ignoring, also known as the cycle proviso. There is an interesting duality between the cycle provisos for the breadth-first search (BFS) and depth first search (DFS) exploration of the state space, which is reflected in the role of the BFS queue and the DFS stack, respectively. The improved version of the algorithm is supported in the current version of Spin and can be shown to perform significantly better than the initial version.

Model Checking is a well-known and fully automatic technique for checking software properties, usually given as temporal logic formulae on the program variables. Most model checkers found in the literature use exact deterministic algorithms to check the properties. These algorithms usually require huge amounts of computational resources if the checked model is large. We propose here the use of a new kind of Ant Colony Optimization (ACO) model, ACOhg, to refute safety properties in concurrent systems. ACO algorithms are stochastic techniques belonging to the class of metaheuristic algorithms and inspired by the foraging behaviour of real ants. The traditional ACO algorithms cannot deal with the model checking problem and thus we use ACOhg to tackle it. The results state that ACOhg algorithms find optimal or near optimal error trails in faulty concurrent systems with a reduced amount of resources, outperforming algorithms that are the state-of-the-art in model checking. This fact makes them suitable for checking safety properties in large concurrent systems, in which traditional techniques fail to find errors because of the model size.

In this paper we analyze the application of parallel and sequential evolutionary algorithms to the automatic test data generation problem. The problem consists of automatically creating a set of input data to test a program. This is a fundamental step in software development and a time consuming task in existing software companies. Canonical sequential evolutionary algorithms have been used in the past for this task. We explore here the use of parallel evolutionary algorithms. Evidence of greater efficiency, larger diversity maintenance, additional availability of memory/CPU, and multi-solution capabilities of the parallel approach, reinforce the importance of the advances in research with these algorithms. We describe in this work how canonical genetic algorithms (GAs) and evolutionary strategies (ESs) can help in software testing, and what the advantages are (if any) of using decentralized populations in these techniques. In addition, we study the influence of some parameters of the proposed test data generator in the results. For the experiments we use a large benchmark composed of twelve programs that includes fundamental algorithms in computer science.

Most approaches for model checking software are based on the generation of abstract models from source code, which may greatly reduce the search space, but may also introduce errors that are not present in the actual program.

In this article we analyze the combination of ACOhg, a new metaheuristic algorithm, plus partial order reduction applied to the problem of finding safety property violations in concurrent models using a model checking approach. ACOhg is a new kind of ant colony optimization algorithm inspired by the foraging behaviour of real ants equipped with internal resorts to search in very large search landscapes. We here apply ACOhg to concurrent models in scenarios located near the edge of the existing knowledge in detecting property violations. The results state that the combination is computationally beneficial for the search and represents a considerable step forward in this field with respect to exact and other heuristic techniques. Key words: Program correctness, ant colony optimization, metaheuristics, model checking, HSF-SPIN

The state explosion problem is the main limitation of model checking. Symmetries in the system being verified can be exploited in order to avoid this problem by defining an equivalence (symmetry) relation on the states of the system, which induces a semantically equivalent quotient system of smaller size. On the other hand, heuristic search algorithms can be applied to improve the bug finding capabilities of model checking. Such algorithms use heuristic functions to guide the exploration. Bestfirst is used for accelerating the search, while A* guarantees optimal error trails if combined with admissible estimates. We analyze some aspects of combining both approaches, concentrating on the problem of finding the optimal path to the equivalence class of a given error state. Experimental results evaluate our approach.

This paper exploits the spatial representation of state space problem graphs to preprocess and enhance heuristic search engines. It combines classical AI exploration with computational geometry. Our case study

Processing (RM-ODP) defines a framework within which support of distribution, interoperability and portability can be integrated. An ODP system is defined in terms of five viewpoints. The ODP engineering specification consists of a set of engineering objects which communicate via a channel object. The engineering viewpoint defines the ODP transparencies and ODP functions. We focus in this paper on the language of the channel engineering object. We associate to each component state of a global state a set of words that may be contained in channels. We define, for each object, a grammar ‘like ’ context free in which, each rule is of the form X → u-1 Yv, where u-1 Yv stand for the residual of the language (L(Y)v) with regard to u. We use context-free grammar properties to make transformations and appear a symbol X in the right member of each X-production to express loop and cycle transitions in the CFSM. The algebraic property of context-free languages is then used to calculate these languages which are minimal solution of a system of equations. These languages can be used to verify some protocol properties such as reachability and deadlock problems.