Abstraction and Learning for Infinite-State Compositional Verification

Despite many advances that enable the application of model checking techniques to the verification of large systems, the state-explosion problem remains the main challenge for scalability. Compositional verification addresses this challenge by decomposing the verification of a large system into the verification of its components. Recent techniques use learning-based approaches to automate compositional verification based on the assume-guarantee style reasoning. However, these techniques are only applicable to finite-state systems. In this work, we propose a new framework that interleaves abstraction and learning to perform automated compositional verification of infinite-state systems. We also discuss the role of learning and abstraction in the related context of interface generation for infinite-state components.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455

MORPHO‐FUNCTIONAL RE‐ESTABLISHMENT OF CRANIO‐FACIAL GROWTH DISORDERS IN PITUITARY DWARFISM BY RHGH THERAPY

The present study evaluates the cranio‐facial growth disorders in a series of patients suffering from pituitary dwarfism, as a result of the therapy with recombinant human growth hormone (rhGH). Included in the study were 15 children diagnosed with pituitary dwarfism in the Endocrinology Clinics of the ”Sf. Spiridon” Hospital of Iasi, subjected to a treatment with rhGH for 2 years. After the application of the therapy, the parameters of general physical development were followed and the dental ortho‐ pantomography and profile cephalometry were analyzed. The results obtained confirm a general physical growth of about 1.3 cm/month in the first year of treatment, followed by values around 1.1 cm/month in the second year. Cranio‐facial development was improved by the increase of both mandibular vertical branch and facial height. At the level of the dental arches, one could observe improved sagital and transversal relations at molar level, as well as a regulating tendency of dental eruption. The therapy with rhGH is thus influent at cranio‐facial level, favourizing the development of maxillaries, regulation of dental eruption and the aesthetic aspects

Compositional Solution Space Quantification for Probabilistic Software Analysis

Probabilistic software analysis aims at quantifying how likely a target event is to occur during program execution. Current approaches rely on symbolic execution to identify the conditions to reach the target event and try to quantify the fraction of the input domain satisfying these conditions. Precise quantification is usually limited to linear constraints, while only approximate solutions can be provided in general through statistical approaches. However, statistical approaches may fail to converge to an acceptable accuracy within a reasonable time. We present a compositional statistical approach for the efficient quantification of solution spaces for arbitrarily complex constraints over bounded floating-point domains. The approach leverages interval constraint propagation to improve the accuracy of the estimation by focusing the sampling on the regions of the input domain containing the sought solutions. Preliminary experiments show significant improvement on previous approaches both in results accuracy and analysis time

Assume-Guarantee Abstraction Refinement for Probabilistic Systems

We describe an automated technique for assume-guarantee style checking of strong simulation between a system and a specification, both expressed as non-deterministic Labeled Probabilistic Transition Systems (LPTSes). We first characterize counterexamples to strong simulation as "stochastic" trees and show that simpler structures are insufficient. Then, we use these trees in an abstraction refinement algorithm that computes the assumptions for assume-guarantee reasoning as conservative LPTS abstractions of some of the system components. The abstractions are automatically refined based on tree counterexamples obtained from failed simulation checks with the remaining components. We have implemented the algorithms for counterexample generation and assume-guarantee abstraction refinement and report encouraging results.Comment: 23 pages, conference paper with full proof

Closed-loop Analysis of Vision-based Autonomous Systems : A Case Study

Deep neural networks (DNNs) are increasingly used in safety-critical autonomous systems as perception components processing high-dimensional image data. Formal analysis of these systems is particularly challenging due to the complexity of the perception DNNs, the sensors (cameras), and the environment conditions. We present a case study applying formal probabilistic analysis techniques to an experimental autonomous system that guides airplanes on taxiways using a perception DNN. We address the above challenges by replacing the camera and the network with a compact probabilistic abstraction built from the confusion matrices computed for the DNN on a representative image data set. We also show how to leverage local, DNN-specific analyses as run-time guards to increase the safety of the overall system. Our findings are applicable to other autonomous systems that use complex DNNs for perception

Association of Under-Approximation Techniques for Generating Tests from Models

International audienceIn this paper we present a Model-Based Testing approach with which we generate tests from an abstraction of a source behavioural model. We show a new algorithm that computes the abstraction as an under-approximation of the source model. Our first contribution is to combine two previous approaches proposed by Ball and Pasareanu et al. to compute May, Must+ and Must- abstract transition relations. Prooftechniques are used to compute these transition relations. The tests obtained by covering the abstract transitions have to be instantiated from the source model. So, following Pasareanu et al., our algorithm additionally computes a concrete transition relation: the tests obtained as sequences of concrete transitions need not be instantiated from the source model. Another contribution is to propose a choice of relevant paramaters and heuristics to pilot the tests computation. We experiment our approach and compare it with a previous approach of ours to compute tests from an abstraction that over-approximates the source model