Extending Nunchaku to Dependent Type Theory

Nunchaku is a new higher-order counterexample generator based on a sequence of transformations from polymorphic higher-order logic to first-order logic. Unlike its predecessor Nitpick for Isabelle, it is designed as a stand-alone tool, with frontends for various proof assistants. In this short paper, we present some ideas to extend Nunchaku with partial support for dependent types and type classes, to make frontends for Coq and other systems based on dependent type theory more useful.Comment: In Proceedings HaTT 2016, arXiv:1606.0542

Integrating Testing and Interactive Theorem Proving

Using an interactive theorem prover to reason about programs involves a sequence of interactions where the user challenges the theorem prover with conjectures. Invariably, many of the conjectures posed are in fact false, and users often spend considerable effort examining the theorem prover's output before realizing this. We present a synergistic integration of testing with theorem proving, implemented in the ACL2 Sedan (ACL2s), for automatically generating concrete counterexamples. Our method uses the full power of the theorem prover and associated libraries to simplify conjectures; this simplification can transform conjectures for which finding counterexamples is hard into conjectures where finding counterexamples is trivial. In fact, our approach even leads to better theorem proving, e.g. if testing shows that a generalization step leads to a false conjecture, we force the theorem prover to backtrack, allowing it to pursue more fruitful options that may yield a proof. The focus of the paper is on the engineering of a synergistic integration of testing with interactive theorem proving; this includes extending ACL2 with new functionality that we expect to be of general interest. We also discuss our experience in using ACL2s to teach freshman students how to reason about their programs.Comment: In Proceedings ACL2 2011, arXiv:1110.447

Automated Random Testing of Numerical Constrained Types

International audienceWe propose an automated testing framework based on constraint programming techniques. Our framework allows the developer to attach a numerical constraint to a type that restricts its set of possible values. We use this constraint as a partial specification of the program, our goal being to derive property-based tests on such annotated programs. To achieve this, we rely on the user-provided constraints on the types of a program: for each function f present in the program, that returns a constrained type, we generate a test. The tests consists of generating uniformly pseudo-random inputs and checking whether f 's output satisfies the constraint. We are able to automate this process by providing a set of generators for primitive types and generator combinators for composite types. To derive generators for constrained types, we present in this paper a technique that characterizes their inhabitants as the solution set of a numerical CSP. This is done by combining abstract interpretation and constraint solving techniques that allow us to efficiently and uniformly generate solutions of numerical CSP. We validated our approach by implementing it as a syntax extension for the OCaml language

Foundational Property-Based Testing

International audienceIntegrating property-based testing with a proof assistant creates an interesting opportunity: reusable or tricky testing code can be formally verified using the proof assistant itself. In this work we introduce a novel methodology for formally verified property-based testing and implement it as a foundational verification framework for QuickChick, a port of QuickCheck to Coq. Our framework enables one to verify that the executable testing code is testing the right Coq property. To make verification tractable, we provide a systematic way for reasoning about the set of outcomes a random data generator can produce with non-zero probability, while abstracting away from the actual probabilities. Our framework is firmly grounded in a fully verified implementation of QuickChick itself, using the same underlying verification methodology. We also apply this methodology to a complex case study on testing an information-flow control abstract machine, demonstrating that our verification methodology is modular and scalable and that it requires minimal changes to existing code

Trusted Software within Focal

International audienceThis paper describes the Integrated Development Environment Focal together with a brief proof of usability on the formal development of access control policies. Focal is an IDE providing powerful functional and object-oriented features that allow to formally express specification and to go step by step (in an incremental approach) to design and implement while proving that the implementation meets its specification or design requirements. These features are particularly well-suited to develop libraries for secure applications

Formal Methods for Constraint-Based Testing and Reversible Debugging in Erlang

Tesis por compendio[ES] Erlang es un lenguaje de programación funcional con concurrencia mediante paso de mensajes basado en el modelo de actores. Éstas y otras características lo hacen especialmente adecuado para aplicaciones distribuidas en tiempo real acrítico. En los últimos años, la popularidad de Erlang ha aumentado debido a la demanda de servicios concurrentes. No obstante, desarrollar sistemas Erlang libres de errores es un reto considerable. A pesar de que Erlang evita muchos problemas por diseño (por ejemplo, puntos muertos), algunos otros problemas pueden aparecer. En este contexto, las técnicas de testing y depuración basadas en métodos formales pueden ser útiles para detectar, localizar y arreglar errores de programación en Erlang. En esta tesis proponemos varios métodos para testing y depuración en Erlang. En particular, estos métodos están basados en modelos semánticos para concolic testing, pruebas basadas en propiedades, depuración reversible con consistencia causal y repetición reversible con consistencia causal de programas Erlang. Además, probamos formalmente las principales propiedades de nuestras propuestas y diseñamos herramientas de código abierto que implementan estos métodos.[CA] Erlang és un llenguatge de programació funcional amb concurrència mitjançant pas de missatges basat en el model d'actors. Estes i altres característiques el fan especialment adequat per a aplicacions distribuïdes en temps real acrític. En els últims anys, la popularitat d'Erlang ha augmentat degut a la demanda de servicis concurrents. No obstant, desenvolupar sistemes Erlang lliures d'errors és un repte considerable. Encara que Erlang evita molts problemes per disseny (per exemple, punts morts), alguns altres problemes poden aparéixer. En este context, les tècniques de testing y depuració basades en mètodes formals poden ser útils per a detectar, localitzar y arreglar errors de programació en Erlang. En esta tesis proposem diversos mètodes per a testing i depuració en Erlang. En particular, estos mètodes estan basats en models semàntics per a concolic testing, testing basat en propietats, depuració reversible amb consistència causal i repetició reversible amb consistència causal de programes Erlang. A més, provem formalment les principals propietats de les nostres propostes i dissenyem ferramentes de codi obert que implementen estos mètodes.[EN] Erlang is a message-passing concurrent, functional programming language based on the actor model. These and other features make it especially appropriate for distributed, soft real-time applications. In the recent years, Erlang's popularity has increased due to the demand for concurrent services. However, developing error-free systems in Erlang is quite a challenge. Although Erlang avoids many problems by design (e.g., deadlocks), some other problems may appear. Here, testing and debugging techniques based on formal methods may be helpful to detect, locate and fix programming errors in Erlang. In this thesis we propose several methods for testing and debugging in Erlang. In particular, these methods are based on semantics models for concolic testing, property-based testing, causal-consistent reversible debugging and causal-consistent replay debugging of Erlang programs. We formally prove the main properties of our proposals and design open-source tools that implement these methods.Palacios Corella, A. (2020). Formal Methods for Constraint-Based Testing and Reversible Debugging in Erlang [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/139076TESISCompendi

Spatial Big Data Analytics: Classification Techniques for Earth Observation Imagery

University of Minnesota Ph.D. dissertation. August 2016. Major: Computer Science. Advisor: Shashi Shekhar. 1 computer file (PDF); xi, 120 pages.Spatial Big Data (SBD), e.g., earth observation imagery, GPS trajectories, temporally detailed road networks, etc., refers to geo-referenced data whose volume, velocity, and variety exceed the capability of current spatial computing platforms. SBD has the potential to transform our society. Vehicle GPS trajectories together with engine measurement data provide a new way to recommend environmentally friendly routes. Satellite and airborne earth observation imagery plays a crucial role in hurricane tracking, crop yield prediction, and global water management. The potential value of earth observation data is so significant that the White House recently declared that full utilization of this data is one of the nation's highest priorities. However, SBD poses significant challenges to current big data analytics. In addition to its huge dataset size (NASA collects petabytes of earth images every year), SBD exhibits four unique properties related to the nature of spatial data that must be accounted for in any data analysis. First, SBD exhibits spatial autocorrelation effects. In other words, we cannot assume that nearby samples are statistically independent. Current analytics techniques that ignore spatial autocorrelation often perform poorly such as low prediction accuracy and salt-and-pepper noise (i.e., pixels predicted as different from neighbors by mistake). Second, spatial interactions are not isotropic and vary across directions. Third, spatial dependency exists in multiple spatial scales. Finally, spatial big data exhibits heterogeneity, i.e., identical feature values may correspond to distinct class labels in different regions. Thus, learned predictive models may perform poorly in many local regions. My thesis investigates novel SBD analytics techniques to address some of these challenges. To date, I have been mostly focusing on the challenges of spatial autocorrelation and anisotropy via developing novel spatial classification models such as spatial decision trees for raster SBD (e.g., earth observation imagery). To scale up the proposed models, I developed efficient learning algorithms via computational pruning. The proposed techniques have been applied to real world remote sensing imagery for wetland mapping. I also had developed spatial ensemble learning framework to address the challenge of spatial heterogeneity, particularly the class ambiguity issues in geographical classification, i.e., samples with the same feature values belong to different classes in different spatial zones. Evaluations on three real world remote sensing datasets confirmed that proposed spatial ensemble learning outperforms current approaches such as bagging, boosting, and mixture of experts when class ambiguity exists

Actes des Cinquièmes journées nationales du Groupement De Recherche CNRS du Génie de la Programmation et du Logiciel

National audienceCe document contient les actes des Cinquièmes journées nationales du Groupement De Recherche CNRS du Gé}nie de la Programmation et du Logiciel (GDR GPL) s'étant déroulées à Nancy du 3 au 5 avril 2013. Les contributions présentées dans ce document ont été sélectionnées par les différents groupes de travail du GDR. Il s'agit de résumés, de nouvelles versions, de posters et de démonstrations qui correspondent à des travaux qui ont déjà été validés par les comités de programmes d'autres conférences et revues et dont les droits appartiennent exclusivement à leurs auteurs

Random Testing For Language Design

Property-based random testing can facilitate formal verification, exposing errors early on in the proving process and guiding users towards correct specifications and implementations. However, effective random testing often requires users to write custom generators for well-distributed random data satisfying complex logical predicates, a task which can be tedious and error prone. In this work, I aim to reduce the cost of property-based testing by making such generators easier to write, read and maintain. I present a domain-specific language, called Luck, in which generators are conveniently expressed by decorating predicates with lightweight annotations to control both the distribution of generated values and the amount of constraint solving that happens before each variable is instantiated. I also aim to increase the applicability of testing to formal verification by bringing advanced random testing techniques to the Coq proof assistant. I describe QuickChick, a QuickCheck clone for Coq, and improve it by incorporating ideas explored in the context of Luck to automatically derive provably correct generators for data constrained by inductive relations. Finally, I evaluate both QuickChick and Luck in a variety of complex case studies from programming languages literature, such as information-flow abstract machines and type systems for lambda calculi

Model Checking and Model-Based Testing : Improving Their Feasibility by Lazy Techniques, Parallelization, and Other Optimizations

This thesis focuses on the lightweight formal method of model-based testing for checking safety properties, and derives a new and more feasible approach. For liveness properties, dynamic testing is impossible, so feasibility is increased by specializing on an important class of properties, livelock freedom, and deriving a more feasible model checking algorithm for it. All mentioned improvements are substantiated by experiments