Automatic Verification of Erlang-Style Concurrency

This paper presents an approach to verify safety properties of Erlang-style, higher-order concurrent programs automatically. Inspired by Core Erlang, we introduce Lambda-Actor, a prototypical functional language with pattern-matching algebraic data types, augmented with process creation and asynchronous message-passing primitives. We formalise an abstract model of Lambda-Actor programs called Actor Communicating System (ACS) which has a natural interpretation as a vector addition system, for which some verification problems are decidable. We give a parametric abstract interpretation framework for Lambda-Actor and use it to build a polytime computable, flow-based, abstract semantics of Lambda-Actor programs, which we then use to bootstrap the ACS construction, thus deriving a more accurate abstract model of the input program. We have constructed Soter, a tool implementation of the verification method, thereby obtaining the first fully-automatic, infinite-state model checker for a core fragment of Erlang. We find that in practice our abstraction technique is accurate enough to verify an interesting range of safety properties. Though the ACS coverability problem is Expspace-complete, Soter can analyse these verification problems surprisingly efficiently.Comment: 12 pages plus appendix, 4 figures, 1 table. The tool is available at http://mjolnir.cs.ox.ac.uk/soter

Abstract verification and debugging of constraint logic programs

The technique of Abstract Interpretation [13] has allowed the development of sophisticated program analyses which are provably correct and practical. The semantic approximations produced by such analyses have been traditionally applied to optimization during program compilation. However, recently, novel and promising applications of semantic approximations have been proposed in the more general context of program verification and debugging [3],[10],[7]

Robustness Verification of k-Nearest Neighbor Classifiers by Abstract Interpretation

openAbstract interpretation is an established mathematical framework introduced by Cousot and Cousot in 1977 and ubiquitously used in static program analysis. In recent years, many noteworthy works have shown how abstract interpretation can be successfully applied to formally verify robustness properties of some major machine learning techniques like (deep) neural networks, decision trees and support vector machines. This research work aims to pursue this line of research by proposing a novel abstract interpretation-based framework for designing a sound abstract version of the k-Nearest Neighbors (kNN) algorithm, a well-known non-parametric supervised learning method widely used for classification and regression tasks, which is then instantiated to the standard interval domain approximating the range of numerical features, to verify its robustness and stability properties. This verification approach has been fully implemented and evaluated on several datasets, including standard benchmark datasets for individual fairness verification, and then compared with some related works finding adversarial examples on kNNs. The experimental results turned out to be very promising and showed high percentages of provable robustness and stability in most of the reference datasets, thus making a step forward in the current state-of-the-art of formal verification of machine learning models.Abstract interpretation is an established mathematical framework introduced by Cousot and Cousot in 1977 and ubiquitously used in static program analysis. In recent years, many noteworthy works have shown how abstract interpretation can be successfully applied to formally verify robustness properties of some major machine learning techniques like (deep) neural networks, decision trees and support vector machines. This research work aims to pursue this line of research by proposing a novel abstract interpretation-based framework for designing a sound abstract version of the k-Nearest Neighbors (kNN) algorithm, a well-known non-parametric supervised learning method widely used for classification and regression tasks, which is then instantiated to the standard interval domain approximating the range of numerical features, to verify its robustness and stability properties. This verification approach has been fully implemented and evaluated on several datasets, including standard benchmark datasets for individual fairness verification, and then compared with some related works finding adversarial examples on kNNs. The experimental results turned out to be very promising and showed high percentages of provable robustness and stability in most of the reference datasets, thus making a step forward in the current state-of-the-art of formal verification of machine learning models

Abstract Interpretation-based verification/certification in the ciaoPP system

CiaoPP is the abstract interpretation-based preprocessor of the Ciao multi-paradigm (Constraint) Logic Programming system. It uses modular, incremental abstract interpretation as a fundamental tool to obtain information about programs. In CiaoPP, the semantic approximations thus produced have been applied to perform high- and low-level optimizations during program compilation, including transformations such as múltiple abstract specialization, parallelization, partial evaluation, resource usage control, and program verification. More recently, novel and promising applications of such semantic approximations are being applied in the more general context of program development such as program verification. In this work, we describe our extensión of the system to incorpórate Abstraction-Carrying Code (ACC), a novel approach to mobile code safety. ACC follows the standard strategy of associating safety certificates to programs, originally proposed in Proof Carrying- Code. A distinguishing feature of ACC is that we use an abstraction (or abstract model) of the program computed by standard static analyzers as a certifícate. The validity of the abstraction on the consumer side is checked in a single-pass by a very efficient and specialized abstractinterpreter. We have implemented and benchmarked ACC within CiaoPP. The experimental results show that the checking phase is indeed faster than the proof generation phase, and that the sizes of certificates are reasonable. Moreover, the preprocessor is based on compile-time (and run-time) tools for the certification of CLP programs with resource consumption assurances

Using abstract interpretation to produce dependable aerospace control software

In the context of software dependability, the software veri- fication process has an important role. Formal verification of programs is an activity that can be inserted in this process to improve software reliability. This paper presents the defini- tion of an approach that employs a formal verification tech- nique based on abstract interpretation. The main goal is to apply this technique as a formal activity in the software veri- fication process to help software engineers identify programs faults. The applicability of the proposed approach is demon- strated by a case study based on embedded aerospace control software. The results obtained from its use show that abstract interpretation can contribute to software dependability.Fundação para a Ciência e a Tecnologia (FCT

Model Checking a Temporal Logic via Program Verification

openThe thesis explores the possibility of viewing Model Checking as an instance of program verification in order to allow for the reuse of the vast theory and toolset of Abstract Interpretation in the setting of Model Checking. Model Checking is a formal verification technique used to analyse the correctness of software systems, based on a representation of the system as a formal model, such as a finite-state machine or a transition system, and on a representation of the properties it must satisfy as temporal logic formulae. On the other hand, Abstract Interpretation is a program analysis method, based on the idea of extracting properties of programs by (over-)approximating their semantics over a so-called abstract domain, typically a complete lattice, whose elements represent program properties. The thesis focuses on ACTL, the universal fragment of the temporal logic CTL, which can describe properties of executions which are universally quantified. It shows how properties expressed in ACTL can be mapped into programs written in a suitable programming language, whose semantics consists of counterexamples to the validity of the formula. Then such a program is analysed by Abstract Interpretation over some abstract domain, exploiting the idea of local completeness as put forward in some recent work, combining lower- and under-approximations.The thesis explores the possibility of viewing Model Checking as an instance of program verification in order to allow for the reuse of the vast theory and toolset of Abstract Interpretation in the setting of Model Checking. Model Checking is a formal verification technique used to analyse the correctness of software systems, based on a representation of the system as a formal model, such as a finite-state machine or a transition system, and on a representation of the properties it must satisfy as temporal logic formulae. On the other hand, Abstract Interpretation is a program analysis method, based on the idea of extracting properties of programs by (over-)approximating their semantics over a so-called abstract domain, typically a complete lattice, whose elements represent program properties. The thesis focuses on ACTL, the universal fragment of the temporal logic CTL, which can describe properties of executions which are universally quantified. It shows how properties expressed in ACTL can be mapped into programs written in a suitable programming language, whose semantics consists of counterexamples to the validity of the formula. Then such a program is analysed by Abstract Interpretation over some abstract domain, exploiting the idea of local completeness as put forward in some recent work, combining lower- and under-approximations

Security Analysis of Role-based Access Control through Program Verification

We propose a novel scheme for proving administrative role-based access control (ARBAC) policies correct with respect to security properties using the powerful abstraction based tools available for program verification. Our scheme uses a combination of abstraction and reduction to program verification to perform security analysis. We convert ARBAC policies to imperative programs that simulate the policy abstractly, and then utilize further abstract-interpretation techniques from program analysis to analyze the programs in order to prove the policies secure. We argue that the aggressive set-abstractions and numerical-abstractions we use are natural and appropriate in the access control setting. We implement our scheme using a tool called VAC that translates ARBAC policies to imperative programs followed by an interval-based static analysis of the program, and show that we can effectively prove access control policies correct. The salient feature of our approach are the abstraction schemes we develop and the reduction of role-based access control security (which has nothing to do with programs) to program verification problems