We present an automatic iterative abstraction-refinement methodology in which the initial abstract model is generated by an automatic analysis of the control structures in the program to be verified. Abstract models may admit erroneous (or "spurious") counterexamples. We devise new symbolic techniques which analyze such counterexamples and refine the abstract model correspondingly.

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We describe a framework for compositional verification of finite state processes. The framework is based on two ideas: a subset of the logic CTL for which satisfaction is preserved under composition; and a preorder on structures which captures the relation between a component and a system containing the component. Satisfaction of a formula in the logic corresponds to being below a particular structure (a tableau for the formula) in the preorder. We show how to do assume-guarantee style reasoning within this framework. In addition, we demonstrate efficient methods for model checking in the logic and for checking the preorder in several special cases. We have implemented a system based on these methods, and we use it to give a compositional verification of a CPU controller. 1 Introduction Temporal logic model checking procedures are useful tools for the verification of finite state systems [3, 12, 20]. However, these procedures have traditionally suffered from the state explosion proble...

This paper describes RacerX, a static tool that uses flowsensitive, interprocedural analysis to detect both race conditions and deadlocks. It is explicitly designed to find errors in large, complex multithreaded systems. It aggressively infers checking information such as which locks protect which operations, which code contexts are multithreaded, and which shared accesses are dangerous. It tracks a set of code features which it uses to sort errors both from most to least severe. It uses novel techniques to counter the impact of analysis mistakes. The tool is fast, requiring between 2-14 minutes to analyze a 1.8 million line system. We have applied it to Linux, FreeBSD, and a large commercial code base, finding serious errors in all of them.

We present a new methodology for automatic verification of C programs against finite state machine specifications. Our approach is compositional, naturally enabling us to decompose the verification of large software systems into subproblems of manageable complexity. The decomposition reflects the modularity in the software design. We use weak simulation as the notion of conformance between the program and its specification. Following the abstractverify-refine paradigm, our tool MAGIC first extracts a finite model from C source code using predicate abstraction and theorem proving. Subsequently, simulation is checked via a reduction to Boolean satisfiability. MAGIC is able to interface with several publicly available theorem provers and SAT solvers. We report experimental results with procedures from the Linux kernel and the OpenSSL toolkit.

This paper presents a novel approach to bug-finding analysis and an implementation of that approach. Our goal is to find as many serious bugs as possible. To do so, we designed a flexible, easy-to-use extension language for specifying analyses and an efficent algorithm for executing these extensions. The language, metal, allows the users of our system to specify a broad class of analyses in terms that resemble the intuitive description of the rules that they check. The system, xgcc, executes these analyses efficiently using a context-sensitive, interprocedural analysis.

Software and knowledge... In this article, we argue that intelligent agents and agent-based systems offer novel opportunities for developing effective tools and techniques. Following a discussion on the classic subject of what makes software complex, we introduce intelligent agents as software structures capable of making "rational decisions". Such rational decision-makers are well-suited to the construction of certain types of software, which mainstream software engineering has had little success with. We then go on to examine a number of prototype techniques proposed for engineering agent systems, including formal specification and verification methods for agent systems, and techniques for implementing agent specifications

Information systems of the future will have to perform well within ever-changing organizational environments. Unfortunately, existing software development methodologies (object-oriented, structured or otherwise) have traditionally been inspired by programming concepts, not organizational ones, leading to a semantic gap between the software system and its operational environment. To reduce this gap, we propose a software development methodology named Tropos which is founded on concepts used to model early requirements. Our proposal adopts the i* organizational modeling framework, which o#ers the notions of actor, goal and (actor) dependency, and uses these as a foundation to model early and late requirements, architectural and detailed design. The paper outlines Tropos phases through an e-business example, and sketches a formal language which underlies the methodology and is intended to support formal analysis. The methodology seems to complement well proposals for agent-oriented programming platforms.

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We show how model checking and symbolic execution can be used to generate test inputs to achieve structural coverage of code that manipulates complex data structures. We focus on obtaining branch-coverage during unit testing of some of the core methods of the red-black tree implementation in the Java TreeMap library, using the Java PathFinder model checker. Three di#erent test generation techniques will be introduced and compared, namely, straight model checking of the code, model checking used in a black-box fashion to generate all inputs up to a fixed size, and lastly, model checking used during white-box test input generation. The main contribution of this work is to show how e#cient white-box test input generation can be done for code manipulating complex data, taking into account complex method preconditions.