10,393 research outputs found

    Inference with Constrained Hidden Markov Models in PRISM

    Full text link
    A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. Defining HMMs with side-constraints in Constraint Logic Programming have advantages in terms of more compact expression and pruning opportunities during inference. We present a PRISM-based framework for extending HMMs with side-constraints and show how well-known constraints such as cardinality and all different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment

    Automated unique input output sequence generation for conformance testing of FSMs

    Get PDF
    This paper describes a method for automatically generating unique input output (UIO) sequences for FSM conformance testing. UIOs are used in conformance testing to verify the end state of a transition sequence. UIO sequence generation is represented as a search problem and genetic algorithms are used to search this space. Empirical evidence indicates that the proposed method yields considerably better (up to 62% better) results compared with random UIO sequence generation

    A Model-Derivation Framework for Software Analysis

    Full text link
    Model-based verification allows to express behavioral correctness conditions like the validity of execution states, boundaries of variables or timing at a high level of abstraction and affirm that they are satisfied by a software system. However, this requires expressive models which are difficult and cumbersome to create and maintain by hand. This paper presents a framework that automatically derives behavioral models from real-sized Java programs. Our framework builds on the EMF/ECore technology and provides a tool that creates an initial model from Java bytecode, as well as a series of transformations that simplify the model and eventually output a timed-automata model that can be processed by a model checker such as UPPAAL. The framework has the following properties: (1) consistency of models with software, (2) extensibility of the model derivation process, (3) scalability and (4) expressiveness of models. We report several case studies to validate how our framework satisfies these properties.Comment: In Proceedings MARS 2017, arXiv:1703.0581

    Verified compilation and optimization of floating-point kernels

    Get PDF
    When verifying safety-critical code on the level of source code, we trust the compiler to produce machine code that preserves the behavior of the source code. Trusting a verified compiler is easy. A rigorous machine-checked proof shows that the compiler correctly translates source code into machine code. Modern verified compilers (e.g. CompCert and CakeML) have rich input languages, but only rudimentary support for floating-point arithmetic. In fact, state-of-the-art verified compilers only implement and verify an inflexible one-to-one translation from floating-point source code to machine code. This translation completely ignores that floating-point arithmetic is actually a discrete representation of the continuous real numbers. This thesis presents two extensions improving floating-point arithmetic in CakeML. First, the thesis demonstrates verified compilation of elementary functions to floating-point code in: Dandelion, an automatic verifier for polynomial approximations of elementary functions; and libmGen, a proof-producing compiler relating floating-point machine code to the implemented real-numbered elementary function. Second, the thesis demonstrates verified optimization of floating-point code in: Icing, a floating-point language extending standard floating-point arithmetic with optimizations similar to those used by unverified compilers, like GCC and LLVM; and RealCake, an extension of CakeML with Icing into the first fully verified optimizing compiler for floating-point arithmetic.Bei der Verifizierung von sicherheitsrelevantem Quellcode vertrauen wir dem Compiler, dass er Maschinencode ausgibt, der sich wie der Quellcode verhƤlt. Man kann ohne weiteres einem verifizierten Compiler vertrauen. Ein rigoroser maschinen-Ć¼}berprĆ¼fter Beweis zeigt, dass der Compiler Quellcode in korrekten Maschinencode Ć¼bersetzt. Moderne verifizierte Compiler (z.B. CompCert und CakeML) haben komplizierte Eingabesprachen, aber unterstĆ¼tzen Gleitkommaarithmetik nur rudimentƤr. De facto implementieren und verifizieren hochmoderne verifizierte Compiler fĆ¼r Gleitkommaarithmetik nur eine starre eins-zu-eins Ɯbersetzung von Quell- zu Maschinencode. Diese Ɯbersetzung ignoriert vollstƤndig, dass Gleitkommaarithmetik eigentlich eine diskrete ReprƤsentation der kontinuierlichen reellen Zahlen ist. Diese Dissertation prƤsentiert zwei Erweiterungen die Gleitkommaarithmetik in CakeML verbessern. Zuerst demonstriert die Dissertation verifizierte Ɯbersetzung von elementaren Funktionen in Gleitkommacode mit: Dandelion, einem automatischen Verifizierer fĆ¼r Polynomapproximierungen von elementaren Funktionen; und libmGen, einen Beweis-erzeugenden Compiler der Gleitkommacode in Relation mit der implementierten elementaren Funktion setzt. Dann demonstriert die Dissertation verifizierte Optimierung von Gleitkommacode mit: Icing, einer Gleitkommasprache die Gleitkommaarithmetik mit Optimierungen erweitert die Ƥhnlich zu denen in unverifizierten Compilern, wie GCC und LLVM, sind; und RealCake, eine Erweiterung von CakeML mit Icing als der erste vollverifizierte Compiler fĆ¼r Gleitkommaarithmetik

    A Model-Derivation Framework for Software Analysis

    Get PDF
    Model-based verification allows to express behavioral correctness conditions like the validity of execution states, boundaries of variables or timing at a high level of abstraction and affirm that they are satisfied by a software system. However, this requires expressive models which are difficult and cumbersome to create and maintain by hand. This paper presents a framework that automatically derives behavioral models from real-sized Java programs. Our framework builds on the EMF/ECore technology and provides a tool that creates an initial model from Java bytecode, as well as a series of transformations that simplify the model and eventually output a timed-automata model that can be processed by a model checker such as UPPAAL. The framework has the following properties: (1) consistency of models with software, (2) extensibility of the model derivation process, (3) scalability and (4) expressiveness of models. We report several case studies to validate how our framework satisfies these properties.Comment: In Proceedings MARS 2017, arXiv:1703.0581

    Parameterized Synthesis

    Full text link
    We study the synthesis problem for distributed architectures with a parametric number of finite-state components. Parameterized specifications arise naturally in a synthesis setting, but thus far it was unclear how to detect realizability and how to perform synthesis in a parameterized setting. Using a classical result from verification, we show that for a class of specifications in indexed LTL\X, parameterized synthesis in token ring networks is equivalent to distributed synthesis in a network consisting of a few copies of a single process. Adapting a well-known result from distributed synthesis, we show that the latter problem is undecidable. We describe a semi-decision procedure for the parameterized synthesis problem in token rings, based on bounded synthesis. We extend the approach to parameterized synthesis in token-passing networks with arbitrary topologies, and show applicability on a simple case study. Finally, we sketch a general framework for parameterized synthesis based on cutoffs and other parameterized verification techniques.Comment: Extended version of TACAS 2012 paper, 29 page
    • ā€¦
    corecore