SERVICE oriented architecture (SOA) is an architectural style to structure software systems, fostering loose coupling and dynamic integration among the applications. The use of SOA and Web services to develop complex and large business processes demands more formal and systematic testing. In addition, characteristics of this type of software limit the straightforward application of traditional testing techniques. Model-based testing (MBT) is a promising approach to deal with these problems. This dissertation investigates how two modeling techniques, namely Finite State Machine (FSM) and Event Sequence Graph (ESG), can be used to support MBT of service-oriented applications. Both techniques model different aspects and can be applied in a complementary way. Initially, we define an MBT process for service-oriented applications that employs FSMs. Based on previous experience, we propose a model-based approach to test composite services using ESGs. This approach is holistic, once test suites are generated to cover both desired situations (positive testing) and unexpected behaviors (negative testing). Three experimental studies evaluate the proposed approach: (i) a case study, (ii) a cost analysis, and (iii) a study in industry. Testing tools are also presented to support its practical use / A Arquitetura orientada a serviço (SOA) é um estilo arquitetural para estruturar sistemas de software de modo que exista um baixo grau de acoplamento entre as aplicações e essas possam ser facilmente integradas de forma dinâmica. A incorporação de SOA e serviços Web em sistemas que modelam processos de negócios grandes e complexos contribui para a necessidade de testes mais formais e sistemáticos. Além disso, características próprias dessa nova classe de software fazem com que técnicas de teste tradicionais não possam ser diretamente aplicadas. O teste baseado em modelo (TBM) apresenta-se como uma abordagem promissora que busca a resolução desses problemas. Esta tese investiga como duas técnicas de modelagem, Máquina de Estados Finitos (MEF) e Grafo de Sequência de Eventos (GSE), podem ser utilizadas para apoiar o TBM de aplicações orientadas a serviço. Essas técnicas modelam diferentes aspectos e podem ser aplicadas de forma complementar. Inicialmente, é definido um processo de TBM para aplicações orientadas a serviço que emprega MEFs. Com base na experiência adquirida, é proposta uma abordagem baseada em modelo para o teste de serviços compostos usando GSEs. Essa abordagem é holística uma vez que conjuntos de teste são gerados para cobrir tanto situações desejadas (teste positivo) quanto comportamentos inesperados (teste negativo). Três estudos experimentais avaliam a abordagem proposta: (i) um estudo de caso, (ii) uma análise de custo e (ii) um estudo na indústria. Ferramentas de teste também são apresentadas para apoiar o uso prático da abordagem proposta

Dans une composition de services Web basée protocole, un ensemble de services composants se collaborent pour donner lieu à un service Composite. Chaque service est représenté par un automate à états finis (AEF). Au sein d’un AEF, chaque transition exprime l’exécution d’une opération qui fait avancer le service vers un état suivant. Une exécution du composite correspond à une séquence de transitions où chacune est déléguée à un des composants. Lors de l’exécution du composite, un ou plusieurs composants peuvent devenir indisponibles. Ceci peut produire une exécution incomplète du composite, et de ce fait un recouvrement est nécessaire. Le recouvrement consiste à transformer l’exécution incomplète en une exécution alternative ayant encore la capacité d’aller vers un état final. La transformation s'effectue en compensant certaines transitions et exécutant d’autres. Cette thèse présente une étude formelle du problème de recouvrement dans une composition de service Web basée protocole. Le problème de recouvrement consiste à trouver une meilleure exécution alternative parmi celles disponibles. Une meilleure alternative doit être atteignable à partir de l’exécution incomplète avec un nombre minimal de compensations visibles (vis-à-vis le client). Pour une exécution alternative donnée, nous prouvons que le problème de décision associé au calcul du nombre de transitions invisiblement compensées est NP-Complet. De ce fait, nous concluons que le problème de décision associé au recouvrement appartient à la classe ΣP2. / In a protocol-based Web service composition, a set of available component services collaborate together in order to provide a new composite service. Services export their protocols as finite state machines (FSMs). A transition in the FSM represents a task execution that makes the service moving to a next state. An execution of the composite corresponds to a sequence of transitions where each task is delegated to a component service. During composite run, one or more delegated components may become unavailable due to hard or soft problems on the Network. This unavailability may result in a failed execution of the composite. We provide in this thesis a formal study of the automatic recovery problem in the protocol-based Web service composition. Recovery consists in transforming the failed execution into a recovery execution. Such a transformation is performed by compensating some transitions and executing some others. The recovery execution is an alternative execution of the composite that still has the ability to reach a final state. The recovery problem consists then in finding the best recovery execution(s) among those available. The best recovery execution is attainable from the failed execution with a minimal number of visible compensations with respect to the client. For a given recovery execution, we prove that the decision problem associated with computing the number of invisibly-compensated transitions is NP-complete. Thus, we conclude that deciding of the best recovery execution is in ΣP2.

This thesis considers control systems governed by autonomous decision-makers and humans. We formalise and compute low-complex control policies with applications to large-scale systems, and propose human interaction models for controllers to compute interaction-aware decisions. In the first part of the thesis, we consider complexity-aware decision-making, formalising the complexity of control policies and constructing algorithms that compute low-complexity control policies. More precisely, first, we consider large-scale control systems given by hierarchical finite state machines (HFSMs) and present a planning algorithm for such systems that exploits the hierarchy to compute optimal policies efficiently. The algorithm can also handle changes in the system with ease. We prove these properties and conduct simulations on HFSMs with up to 2 million states, including a robot application, where our algorithm outperforms both Dijkstra's algorithm and Contraction Hierarchies.  Second, we present a planning objective for control systems modelled as finite state machines yielding an explicit trade-off between a policy's performance and complexity. We consider Kolmogorov complexity since it captures the ultimate compression of an object on a universal Turing machine. We prove that this trade-off is hard to optimise in the sense that dynamic programming is infeasible. Nonetheless, we present two heuristic algorithms obtaining low-complexity policies and evaluate the algorithms on a simple navigation task for a mobile robot, where we obtain low-complexity policies that concur with intuition.  In the second part of the thesis, we consider human-in-the-loop systems and predict human decision-making in such systems. First, we look at how the interaction between a robot and a human in a control system can be predicted using game theory, focusing on an autonomous truck platoon interacting with a human-driven car. The interaction is modelled as a hierarchical dynamic game, where the hierarchical decomposition is temporal with a high-fidelity tactical horizon predicting immediate interactions and a low-fidelity strategic horizon estimating long-term behaviour. The game enables feasible computations validated through simulations yielding situation-aware behaviour with natural and safe interactions.  Second, we seek models to explain human decision-making, focusing on driver overtaking scenarios. The overtaking problem is formalised as a decision problem with perceptual uncertainty. We propose and numerically analyse risk-agnostic and risk-aware decision models, judging if an overtaking is desirable. We show how a driver's decision time and confidence level can be characterised through two model parameters, which collectively represent human risk-taking behaviour. We detail an experimental testbed for evaluating the decision-making process in the overtaking scenario and present some preliminary experimental results from two human drivers. / Denna avhandling studerar styrsystem med autonoma beslutsfattare och människor. Vi formaliserar och beräknar styrlagar av låg komplexitet med tillämpningar på storskaliga system samt föreslår modeller för mänsklig interaktion som kan användas av regulatorer för att beräkna interaktionsmedvetna beslut. I den första delen av denna avhandling studerar vi komplexitet-medveten beslutsfattning, där vi formaliserar styrlagars komplexitet samt konstruerar algoritmer som beräknar styrlagar med låg komplexitet. Mer precist, först studerar vi storskaliga system givna av hierarkiska finita tillståndsmaskiner (HFSMs) och presenterar en planeringsalgoritm för sådana system som utnyttjar hierarkin för att beräkna optimala styrlagar effektivt. Algoritmen kan också lätt hantera förändringar i systemet. Vi bevisar dessa egenskaper och utför simuleringar på HFSMs med upp till 2 miljoner tillstånd, inklusive en robot-applikation, där vår algorithm överträffar både Dijkstra's algoritm och så kallade Contraction Hierarchies. För det andra så presenterar vi ett planeringsobjektiv för finita tillståndsmaskiner som ger en explicit avvägning mellan ett styrlags prestanda och komplexitet. Vi använder Kolmogorovkomplexitet då den fångar den ultimata komprimeringen av ett objekt i en universell Turing-maskin. Vi bevisar att detta objektiv är icke-trivial att optimera över i avseendet att dynamisk programming är omöjligt att utföra. Vi presenterar två algoritmer som beräknar styrlagar med låg komplexitet och evaluerar våra algoritmer på ett enkelt navigationsproblem där vi erhåller styrlagar av låg komplexitet som instämmer med intuition. I den andra delen av denna avhandling behandlar vi reglersystem där en människa interagerar med systemet och studerar hur mänskligt beslutsfattande i sådana system kan förutspås. Först studerar vi hur interaktionen mellan en maskin och en människa i ett reglersystem can förutspås med hjälp av spelteori, med fokus på en självkörande lastbilskonvoj som interagerar med en mänskligt styrd bil. Interaktionen är modellerad som ett hierarkiskt dynamiskt spel, där den hierarkiska indelningen är tidsmässig med en högupplöst taktil horisont som förutspår omedelbara interaktioner samt en lågupplöst strategisk horisont som estimerar långtgående interaktioner. Indelning möjliggör beräkningar som vi validerar via simuleringar där vi får situations-medvetet beteende med naturliga och säkra interaktioner. För det andra söker vi en model med få parametrar som förklarar mänskligt beteende där vi fokuserar på omkörningar. Vi formaliserar omkörningsproblemet som ett beslutfattningsproblem med perceptuell osäkerhet. Vi presenterar och analyserar numeriskt risk-agnostiska och risk-medvetna beslutsmodeller som avväger om en omkörning är önskvärd. Vi visar hur en förares beslutstid och konfidensnivå kan karakteriserar via två modellparametrar som tillsammans representerar mänskligt risk-beteende. Vi beskriver en experimentell testbädd och presentar preliminära resultat från två mänskliga förare. / <p>QC 20230523</p>