26. Theorietag Automaten und Formale Sprachen 23. Jahrestagung Logik in der Informatik: Tagungsband

Der Theorietag ist die Jahrestagung der Fachgruppe Automaten und Formale Sprachen der Gesellschaft für Informatik und fand erstmals 1991 in Magdeburg statt. Seit dem Jahr 1996 wird der Theorietag von einem eintägigen Workshop mit eingeladenen Vorträgen begleitet. Die Jahrestagung der Fachgruppe Logik in der Informatik der Gesellschaft für Informatik fand erstmals 1993 in Leipzig statt. Im Laufe beider Jahrestagungen finden auch die jährliche Fachgruppensitzungen statt. In diesem Jahr wird der Theorietag der Fachgruppe Automaten und Formale Sprachen erstmalig zusammen mit der Jahrestagung der Fachgruppe Logik in der Informatik abgehalten. Organisiert wurde die gemeinsame Veranstaltung von der Arbeitsgruppe Zuverlässige Systeme des Instituts für Informatik an der Christian-Albrechts-Universität Kiel vom 4. bis 7. Oktober im Tagungshotel Tannenfelde bei Neumünster. Während des Tre↵ens wird ein Workshop für alle Interessierten statt finden. In Tannenfelde werden • Christoph Löding (Aachen) • Tomás Masopust (Dresden) • Henning Schnoor (Kiel) • Nicole Schweikardt (Berlin) • Georg Zetzsche (Paris) eingeladene Vorträge zu ihrer aktuellen Arbeit halten. Darüber hinaus werden 26 Vorträge von Teilnehmern und Teilnehmerinnen gehalten, 17 auf dem Theorietag Automaten und formale Sprachen und neun auf der Jahrestagung Logik in der Informatik. Der vorliegende Band enthält Kurzfassungen aller Beiträge. Wir danken der Gesellschaft für Informatik, der Christian-Albrechts-Universität zu Kiel und dem Tagungshotel Tannenfelde für die Unterstützung dieses Theorietags. Ein besonderer Dank geht an das Organisationsteam: Maike Bradler, Philipp Sieweck, Joel Day. Kiel, Oktober 2016 Florin Manea, Dirk Nowotka und Thomas Wilk

On (Omega-)regular model checking

peer reviewedChecking infinite-state systems is frequently done by encoding infinite sets of states as regular languages. Computing such a regular representation of, say, the set of reachable states of a system requires acceleration techniques that can finitely compute the effect of an unbounded number of transitions. Among the acceleration techniques that have been proposed, one finds both specific and generic techniques. Specific techniques exploit the particular type of system being analyzed, for example, a system manipulating queues or integers, whereas generic techniques only assume that the transition relation is represented by a finite-state transducer, which has to be iterated. In this article, we investigate the possibility of using generic techniques in cases where only specific techniques have been exploited so far. Finding that existing generic techniques are often not applicable in cases easily handled by specific techniques, we have developed a new approach to iterating transducers. This new approach builds on earlier work, but exploits a number of new conceptual and algorithmic ideas, often induced with the help of experiments, that give it a broad scope, as well as good performances

A Domain Specific Language for Digital Forensics and Incident Response Analysis

One of the longstanding conceptual problems in digital forensics is the dichotomy between the need for verifiable and reproducible forensic investigations, and the lack of practical mechanisms to accomplish them. With nearly four decades of professional digital forensic practice, investigator notes are still the primary source of reproducibility information, and much of it is tied to the functions of specific, often proprietary, tools. The lack of a formal means of specification for digital forensic operations results in three major problems. Specifically, there is a critical lack of: a) standardized and automated means to scientifically verify accuracy of digital forensic tools; b) methods to reliably reproduce forensic computations (their results); and c) framework for inter-operability among forensic tools. Additionally, there is no standardized means for communicating software requirements between users, researchers and developers, resulting in a mismatch in expectations. Combined with the exponential growth in data volume and complexity of applications and systems to be investigated, all of these concerns result in major case backlogs and inherently reduce the reliability of the digital forensic analyses. This work proposes a new approach to the specification of forensic computations, such that the above concerns can be addressed on a scientific basis with a new domain specific language (DSL) called nugget. DSLs are specialized languages that aim to address the concerns of particular domains by providing practical abstractions. Successful DSLs, such as SQL, can transform an application domain by providing a standardized way for users to communicate what they need without specifying how the computation should be performed. This is the first effort to build a DSL for (digital) forensic computations with the following research goals: 1) provide an intuitive formal specification language that covers core types of forensic computations and common data types; 2) provide a mechanism to extend the language that can incorporate arbitrary computations; 3) provide a prototype execution environment that allows the fully automatic execution of the computation; 4) provide a complete, formal, and auditable log of computations that can be used to reproduce an investigation; 5) demonstrate cloud-ready processing that can match the growth in data volumes and complexity

Second-Order Hyperproperties

We introduce Hyper$^2$LTL, a temporal logic for the specification of hyperproperties that allows for second-order quantification over sets of traces. Unlike first-order temporal logics for hyperproperties, such as HyperLTL, Hyper$^2$LTL can express complex epistemic properties like common knowledge, Mazurkiewicz trace theory, and asynchronous hyperproperties. The model checking problem of Hyper$^2$LTL is, in general, undecidable. For the expressive fragment where second-order quantification is restricted to smallest and largest sets, we present an approximate model-checking algorithm that computes increasingly precise under- and overapproximations of the quantified sets, based on fixpoint iteration and automata learning. We report on encouraging experimental results with our model-checking algorithm, which we implemented in the tool~\texttt{HySO}

CALF: Categorical Automata Learning Framework

Automata learning is a popular technique used to automatically construct an automaton model from queries, and much research has gone into devising specific adaptations of such algorithms for different types of automata. This thesis presents a unifying approach to many existing algorithms using category theory, which eases correctness proofs and guides the design of new automata learning algorithms. We provide a categorical automata learning framework---CALF---that at its core includes an abstract version of the popular L* algorithm. Using this abstract algorithm we derive several concrete ones. We instantiate the framework to a large class of Set functors, by which we recover for the first time a tree automata learning algorithm from an abstract framework, which moreover is the first to cover also algebras of quotiented polynomial functors. We further develop a general algorithm to learn weighted automata over a semiring. On the one hand, we identify a class of semirings, principal ideal domains, for which this algorithm terminates and for which no learning algorithm previously existed; on the other hand, we show that it does not terminate over the natural numbers. Finally, we develop an algorithm to learn automata with side-effects determined by a monad and provide several optimisations, as well as an implementation with experimental evaluation. This allows us to improve existing algorithms and opens the door to learning a wide range of automata

A Functional, Comprehensive and Extensible Multi-Platform Querying and Transformation Approach

This thesis is about a new model querying and transformation approach called FunnyQT which is realized as a set of APIs and embedded domain-specific languages (DSLs) in the JVM-based functional Lisp-dialect Clojure. Founded on a powerful model management API, FunnyQT provides querying services such as comprehensions, quantified expressions, regular path expressions, logic-based, relational model querying, and pattern matching. On the transformation side, it supports the definition of unidirectional model-to-model transformations, of in-place transformations, it supports defining bidirectional transformations, and it supports a new kind of co-evolution transformations that allow for evolving a model together with its metamodel simultaneously. Several properties make FunnyQT unique. Foremost, it is just a Clojure library, thus, FunnyQT queries and transformations are Clojure programs. However, most higher-level services are provided as task-oriented embedded DSLs which use Clojure's powerful macro-system to support the user with tailor-made language constructs important for the task at hand. Since queries and transformations are just Clojure programs, they may use any Clojure or Java library for their own purpose, e.g., they may use some templating library for defining model-to-text transformations. Conversely, like every Clojure program, FunnyQT queries and transformations compile to normal JVM byte-code and can easily be called from other JVM languages. Furthermore, FunnyQT is platform-independent and designed with extensibility in mind. By default, it supports the Eclipse Modeling Framework and JGraLab, and support for other modeling frameworks can be added with minimal effort and without having to modify the respective framework's classes or FunnyQT itself. Lastly, because FunnyQT is embedded in a functional language, it has a functional emphasis itself. Every query and every transformation compiles to a function which can be passed around, given to higher-order functions, or be parametrized with other functions