Use of metaknowledge in the verification of knowledge-based systems

Knowledge-based systems are modeled as deductive systems. The model indicates that the two primary areas of concern in verification are demonstrating consistency and completeness. A system is inconsistent if it asserts something that is not true of the modeled domain. A system is incomplete if it lacks deductive capability. Two forms of consistency are discussed along with appropriate verification methods. Three forms of incompleteness are discussed. The use of metaknowledge, knowledge about knowledge, is explored in connection to each form of incompleteness

Using Graph Transformations and Graph Abstractions for Software Verification

In this paper we describe our intended approach for the verification of software written in imperative programming languages. We base our approach on model checking of graph transition systems, where each state is a graph and the transitions are specified by graph transformation rules. We believe that graph transformation is a very suitable technique to model the execution semantics of languages with dynamic memory allocation. Furthermore, such representation allows us to investigate the use of graph abstractions, which can mitigate the combinatorial explosion inherent to model checking. In addition to presenting our planned approach, we reason about its feasibility, and, by providing a brief comparison to other existing methods, we highlight the benefits and drawbacks that are expected

Your Proof Fails? Testing Helps to Find the Reason

Applying deductive verification to formally prove that a program respects its formal specification is a very complex and time-consuming task due in particular to the lack of feedback in case of proof failures. Along with a non-compliance between the code and its specification (due to an error in at least one of them), possible reasons of a proof failure include a missing or too weak specification for a called function or a loop, and lack of time or simply incapacity of the prover to finish a particular proof. This work proposes a new methodology where test generation helps to identify the reason of a proof failure and to exhibit a counter-example clearly illustrating the issue. We describe how to transform an annotated C program into C code suitable for testing and illustrate the benefits of the method on comprehensive examples. The method has been implemented in STADY, a plugin of the software analysis platform FRAMA-C. Initial experiments show that detecting non-compliances and contract weaknesses allows to precisely diagnose most proof failures.Comment: 11 pages, 10 figure

Applying Formal Methods to Networking: Theory, Techniques and Applications

Despite its great importance, modern network infrastructure is remarkable for the lack of rigor in its engineering. The Internet which began as a research experiment was never designed to handle the users and applications it hosts today. The lack of formalization of the Internet architecture meant limited abstractions and modularity, especially for the control and management planes, thus requiring for every new need a new protocol built from scratch. This led to an unwieldy ossified Internet architecture resistant to any attempts at formal verification, and an Internet culture where expediency and pragmatism are favored over formal correctness. Fortunately, recent work in the space of clean slate Internet design---especially, the software defined networking (SDN) paradigm---offers the Internet community another chance to develop the right kind of architecture and abstractions. This has also led to a great resurgence in interest of applying formal methods to specification, verification, and synthesis of networking protocols and applications. In this paper, we present a self-contained tutorial of the formidable amount of work that has been done in formal methods, and present a survey of its applications to networking.Comment: 30 pages, submitted to IEEE Communications Surveys and Tutorial

A unified approach for static and runtime verification : framework and applications

Static verification of software is becoming ever more effective and efficient. Still, static techniques either have high precision, in which case powerful judgements are hard to achieve automatically, or they use abstractions supporting increased automation, but possibly losing important aspects of the concrete system in the process. Runtime verification has complementary strengths and weaknesses. It combines full precision of the model (including the real deployment environment) with full automation, but cannot judge future and alternative runs. Another drawback of runtime verification can be the computational overhead of monitoring the running system which, although typically not very high, can still be prohibitive in certain settings. In this paper we propose a framework to combine static analysis techniques and runtime verification with the aim of getting the best of both techniques. In particular, we discuss an instantiation of our framework for the deductive theorem prover KeY, and the runtime verification tool Larva. Apart from combining static and dynamic verification, this approach also combines the data centric analysis of KeY with the control centric analysis of Larva. An advantage of the approach is that, through the use of a single specification which can be used by both analysis techniques, expensive parts of the analysis could be moved to the static phase, allowing the runtime monitor to make significant assumptions, dropping parts of expensive checks at runtime. We also discuss specific applications of our approach.peer-reviewe

Test generation for high coverage with abstraction refinement and coarsening (ARC)

Testing is the main approach used in the software industry to expose failures. Producing thorough test suites is an expensive and error prone task that can greatly benefit from automation. Two challenging problems in test automation are generating test input and evaluating the adequacy of test suites: the first amounts to producing a set of test cases that accurately represent the software behavior, the second requires defining appropriate metrics to evaluate the thoroughness of the testing activities. Structural testing addresses these problems by measuring the amount of code elements that are executed by a test suite. The code elements that are not covered by any execution are natural candidates for generating further test cases, and the measured coverage rate can be used to estimate the thoroughness of the test suite. Several empirical studies show that test suites achieving high coverage rates exhibit a high failure detection ability. However, producing highly covering test suites automatically is hard as certain code elements are executed only under complex conditions while other might be not reachable at all. In this thesis we propose Abstraction Refinement and Coarsening (ARC), a goal oriented technique that combines static and dynamic software analysis to automatically generate test suites with high code coverage. At the core of our approach there is an abstract program model that enables the synergistic application of the different analysis components. In ARC we integrate Dynamic Symbolic Execution (DSE) and abstraction refinement to precisely direct test generation towards the coverage goals and detect infeasible elements. ARC includes a novel coarsening algorithm for improved scalability. We implemented ARC-B, a prototype tool that analyses C programs and produces test suites that achieve high branch coverage. Our experiments show that the approach effectively exploits the synergy between symbolic testing and reachability analysis outperforming state of the art test generation approaches. We evaluated ARC-B on industry relevant software, and exposed previously unknown failures in a safety-critical software component

Integration of Static and Dynamic Analysis Techniques for Checking Noninterference

In this article, we present an overview of recent combinations of deductive program verification and automatic test generation on the one hand and static analysis on the other hand, with the goal of checking noninterference. Noninterference is the non-functional property that certain confidential information cannot leak to certain public output, i.e., the confidentiality of that information is always preserved. We define the noninterference properties that are checked along with the individual approaches that we use in different combinations. In one use case, our framework for checking noninterference employs deductive verification to automatically generate tests for noninterference violations with an improved test coverage. In another use case, the framework provides two combinations of deductive verification with static analysis based on system dependence graphs to prove noninterference, thereby reducing the effort for deductive verification

Research in advanced formal theorem-proving techniques

The results are summarised of a project aimed at the design and implementation of computer languages to aid in expressing problem solving procedures in several areas of artificial intelligence including automatic programming, theorem proving, and robot planning. The principal results of the project were the design and implementation of two complete systems, QA4 and QLISP, and their preliminary experimental use. The various applications of both QA4 and QLISP are given