The popular model checker PRISM has been successfully used for the modeling and analysis of complex probabilistic systems. As one way to tackle the challenging state explosion problem, PRISM supports symbolic storage and manipulation using multi-terminal binary decision diagrams for representing the models and in the computations. However, it lacks automated heuristics for variable reordering, even though it is well known that the order of BDD variables plays a crucial role for compact representations and efficient computations. In this article, we present a collection of extensions to PRISM. First, we provide support for automatic variable reordering within the symbolic engines of PRISM and allow users to manually control the variable ordering at a fine-grained level. Second, we provide extensions in the realm of reward-bounded properties, namely symbolic computations of quantiles in Markov decision processes and, for both the explicit and symbolic engines, the approximative computation of quantiles for continuous-time Markov chains as well as support for multi-reward-bounded properties. Finally, we provide an implementation for obtaining minimal weak deterministic Büchi automata for the obligation fragment of linear temporal logic (LTL), with applications for expected accumulated reward computations with a finite horizon given by a co-safe LTL formula.

Safety in human in the loop systems, systems that change behavior with human input, is difficult to achieve. This difficulty can cost lives. As desired system capability grows, so too does the requisite complexity of the system. This complexity can result in designers not accounting for every use case of the system and unintentionally designing in unsafe behavior. Furthermore, complexity of operation and control can result in operators becoming confused during use or receiving insufficient training in the first place. All these cases can result in unsafe operations. One method of improving safety is implementing the use of formal models during the design process. These formal models can be analyzed mathematically to detect dangerous conditions, but can be difficult to produce without time, money, and expertise.<br> This document details the study of potential methods for constructing formal models autonomously from recorded observations of system use, minimizing the need for system expertise, saving time, money, and personnel in this safety critical process. I first discuss how different system characteristics affect system modeling, isolating specific traits that most clearly affect the modeling process Then, I develop a technique for modeling a simple, digital, menu-based system based on a record of user inputs. This technique attempts to measure the availability of different inputs for the user, and then distinguishes states by comparing input availabilities. From there, I compare paths between states and check for shared behaviors. I then expand the general procedure to capture the behavior of a flight simulator. This system more closely resembles real-world safety critical systems and can therefore be used to approximate a real use case of the method outlined. I use machine learning tools for statistical analysis, comparing patterns in system behavior and user behaviors. Last, I discuss general conclusions on how the modeling approaches outlined in this document can be improved and expanded upon.<br> For simple systems, we find that inputs alone can produce state machines, but without corresponding system information, they are less helpful for determining relative safety of different use cases than is needed. Through machine learning, we find that records of complex system use can be decomposed into sets of nominal and anomalous states but determining the causal link between user inputs and transitions between these conditions is not simple and requires further research.

With the widespread use of information systems in modern society comes a growing demand for customizable and adaptable software. As a result, systems are increasingly developed as families of products adapted to specific contexts and requirements. Features are an established concept to capture the commonalities and variability between system variants. Most prominently, the concept is applied in the design, modeling, analysis, and implementation of software product lines where products are built upon a common base and are distinguished by their features. While adaptations encapsulated within features are mainly static and remain part of the system after deployment, dynamic adaptations become increasingly important. Especially interconnected mobile devices and embedded systems are required to be context-sensitive and (self-)adaptive. A promising concept for the design and implementation of such systems are roles as they capture context-dependent and collaboration-specific behavior. A major challenge in the development of feature-oriented and role-based systems are interactions, i.e., emergent behavior that arises from the combination of multiple features or roles. As the number of possible combinations is usually exponential in the number of features and roles, the detection of such interactions is difficult. Since unintended interactions may compromise the functional correctness of a system and may lead to reduced efficiency or reliability, it is desirable to detect them as early as possible in the development process. The goal of this thesis is to adopt the concepts of features and roles in the formal modeling and analysis of systems and system families. In particular, the focus is on the quantitative analysis of operational models by means of probabilistic model checking for supporting the development process and for ensuring correctness. The tool ProFeat, which enables a quantitative analysis of stochastic system families defined in terms of features, has been extended with additional language constructs, support for a one-by-one analysis of system variants, and a symbolic representation of analysis results. The implementation is evaluated by means of several case studies which compare different analysis approaches and show how ProFeat facilitates a family-based quantitative analysis of systems. For the compositional modeling of role-based systems, role-based automata (RBA) are introduced. The thesis presents a modeling language that is based on the input language of the probabilistic model checker PRISM to compactly describe RBA. Accompanying tool support translates RBA models into the PRISM language to enable the formal analysis of functional and non-functional properties, including system dynamics, contextual changes, and interactions. Furthermore, an approach for a declarative and compositional definition of role coordinators based on the exogenous coordination language Reo is proposed. The adequacy of the RBA approach for detecting interactions within context-sensitive and adaptive systems is shown by several case studies.:1 Introduction 1.1 Engineering approaches for variant-rich adaptive systems 1.2 Validation and verification methods 1.3 Analysis of feature-oriented and role-based systems 1.4 Contribution 1.5 Outline 2 Preliminaries I Feature-oriented systems 3 Feature-oriented engineering for family-based analysis 3.1 Feature-oriented development 3.2 Describing system families: The ProFeat language 3.2.1 Feature-oriented language constructs 3.2.2 Parametrization 3.2.3 Metaprogramming language extensions 3.2.4 Property specifications 3.2.5 Semantics 3.3 Implementation 3.3.1 Translation of ProFeat models 3.3.2 Post-processing of analysis results 4 Case studies and application areas 4.1 Comparing family-based and product-based analysis 4.1.1 Analysis of feature-oriented systems 4.1.2 Analysis of parametrized systems 4.2 Software product lines 4.2.1 Body sensor network 4.2.2 Elevator product line 4.3 Self-adaptive systems 4.3.1 Adaptive network system model 4.3.2 Adaptation protocol for distributed systems II Role-based Systems 5 Formal modeling and analysis of role-based systems 5.1 The role concept 5.1.1 Towards a common notion of roles 5.1.2 The Compartment Role Object Model 5.1.3 Roles in programming languages 5.2 Compositional modeling of role-based behavior 5.2.1 Role-based automata and their composition 5.2.2 Algebraic properties of compositions 5.2.3 Coordination and semantics of RBA 6 Implementation of a role-oriented modeling language 6.1 Role-oriented modeling language 6.1.1 Declaration of the system structure 6.1.2 Definition of operational behavior 6.2 Translation of role-based models 6.2.1 Transformation to multi-action MDPs 6.2.2 Multi-action extension of PRISM 6.2.3 Translation of components 6.2.4 Translation of role-playing coordinators 6.2.5 Encoding role-playing into states 7 Exogenous coordination of roles 7.1 The exogenous coordination language Reo 7.2 Constraint automata 7.3 Embedding of role-based automata in constraint automata 7.4 Implementation 7.4.1 Exogenous coordination of PRISM modules 7.4.2 Reo for exogenous coordination within PRISM 8 Evaluation of the role-oriented approach 8.1 Experimental studies 8.1.1 Peer-to-peer file transfer 8.1.2 Self-adaptive production cell 8.1.3 File transfer with exogenous coordination 8.2 Classification 8.3 Related work 8.3.1 Role-based approaches 8.3.2 Aspect-oriented approaches 8.3.3 Feature-oriented approaches 9 Conclusion