004 Datenverarbeitung; Informatik
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The current electricity grid is undergoing major changes. There is increasing pressure to move away from power generation from fossil fuels, both due to ecological concerns and fear of dependencies on scarce natural resources. Increasing the share of decentralized generation from renewable sources is a widely accepted way to a more sustainable power infrastructure. However, this comes at the price of new challenges: generation from solar or wind power is not controllable and only forecastable with limited accuracy. To compensate for the increasing volatility in power generation, exerting control on the demand side is a promising approach. By providing flexibility on demand side, imbalances between power generation and demand may be mitigated.
This work is concerned with developing methods to provide grid support on demand side while limiting the associated costs. This is done in four major steps: first, the target power curve to follow is derived taking both goals of a grid authority and costs of the respective load into account. In the following, the special case of data centers as an instance of significant loads inside a power grid are focused on more closely. Data center services are adapted in a way such as to achieve the previously derived power curve. By means of hardware power demand models, the required adaptation of hardware utilization can be derived. The possibilities of adapting software services are investigated for the special use case of live video encoding. A method to minimize quality of experience loss while reducing power demand is presented. Finally, the possibility of applying probabilistic model checking to a continuous demand-response scenario is demonstrated.
Replacing fossil-fueled vehicles with Electric Vehicles (EVs) poses new challenges for power distribution networks. Specifically speaking, the electrification of the mobility sector relies on the ability to process and analyze information on when, where, for how long, or how fast charging processes will take place. Nevertheless, such kind of information is typically difficult to acquire or insufficiently predictable due to the dynamic nature of the system. Also, the increasing adoption rate of the renewable energy sources, specifically the domestic Photovoltaic (PV) systems, and the potentially associated grid defection scenarios will significantly impact the cost and efforts required to operate the grid in terms of power quality and demand-supply aspects. However, such emerging requirements have arguably not been taken into account when the distribution grid was built originally. Besides, expanding the distribution and transmission capacity is a very costly and lengthy process. Therefore, any proposed solution should be cost-effective as well as environment-, grid- and user-friendly. To this end, the advancements in Information and Communications Technology (ICT) are increasingly adopted and applied. This thesis addresses the rapidly growing EV sector and deals with the problems to overcome potential power quality degradation caused by the challenges mentioned above.
Since time switch and radio ripple control as existing solutions in Germany are costly and neither very effective nor scalable as it requires hardware retrofitting of existing public Charging Stations (CSs), the primary focus of this work is the development of an appropriate, standards-based, scalable, and smart charging solution of EVs. Such a solution can, in turn, boost the usage of renewable energy by ensuring that the existing grid infrastructure can operate within its permissible limits while maintaining acceptable levels of power quality.
This work introduces a new definition of the concept, “grid-friendly EV charging”, where the power demand of a CS is adjusted depending on the real-time status of a power grid. In this regard, the conflicting concerns of stakeholders in an EV ecosystem are considered. For example, a Distribution System Operator (DSO) does not want to reveal a lot of technical details about the power grid or its status. Similarly, a Charging Service Provider (CSP) wants to keep its clients happy without sharing the details of its business model with others, namely, DSOs. For that sake, a distributed smart charging architecture is proposed in this thesis. It is event-driven and responds in nearly real-time to unforeseen and critical grid situations such as high/low voltage, congestion, phase unbalance, and harmonics. In that regard, the publish/subscribe messaging pattern, used as a part of the architecture, enables an efficient and well-performing communication scheme among the different components. Moreover, an indication mechanism about the different issues in a power grid is developed; it adopts the traffic light model. It works as a black box to separate smart controllers for each CS and configured only by the CSP. Smart chargers enable a smooth adjustment of the charging power to avoid drastic changes in the grid state. To that end, two types of intelligent controllers are developed and tested. While the first controller is inspired by the fuzzy logic, the second one is inspired by the slow-start mechanism used in TCP to control congestion in computer networks.
A simulative approach is applied to evaluate the solution, thereby, a topology of a real low voltage grid with realistic load and generation profiles is used. Furthermore, a set of metrics is defined regarding the main concerns of stakeholders: voltage, overloading, fairness, the satisfaction of EV users and grid operator, as well as the grid-friendly behavior of a CS/ EV user. The evaluation shows that the solution is able to guarantee a safe operation of the grid. The proposed system can ensure a grid-friendly charging by sacrificing of a small portion of user satisfaction, that sacrifice of a user is awarded via a points-based reward system. Last but not least, the proposed distributed controllers are compared to two other controllers: (1) a decentralized controller based only on sensing the local voltage and (2) a very strict centralized controller focusing on grid-friendliness. The latter ensures proportional fairness among users regarding the objective function of the optimization problem solved in each simulation step. The distributed controllers are superior to the decentralized controller in terms of grid friendly and fairness and converge in general to the centralized one.
Online social networks provide a rich source of information about millions of users worldwide. However, due to sparsity and complex structure, analyzing these networks is quite challenging and expensive. Recently, graph embedding emerged to map networked data into low-dimensional representations, i.e. vector embeddings. These representations are fed into off-the-shelf machine learning algorithms to simplify and speed up graph analytic tasks. Given the immense importance of social network analysis, in this thesis, we aim to study graph embedding for social networks in three directions.
Firstly, we focus on social networks at microscopic level to primarily encode the structural characteristic of users' personal networks so-called ego networks. These representations are utilized in evaluation tasks whose performance depends on relational information from direct neighbors. For example, social circle prediction and event attendance inference both need structural information from neighbors in social networks.
Secondly, we explore assessing the content of vector embeddings in terms of topological properties. This could be explained via two proposed approaches: 1) a learning to rank algorithm in which the model weights reveal the importance of properties at subgraph level (ego networks), 2) a regression model for direct approximation of network statistical properties at vertex level.
Thirdly, we propose extensions of graph embedding to capture sign or additional content of social networks. Users in social media often express their feelings and attitudes towards others which forms sentiment links besides social links. We design a joint objective function whose terms capture semantics of both social and sentiment links simultaneously. We also propose a multi-task learning framework for networks with attributes and labels by stacking autoencoders. The weights of the learning tasks are automatically assigned via an adaptive loss weighting layer.
The concept of programmable networks is radically changing the way communication infrastructures are designed, integrated, and operated. Currently, the topic is spearheaded by concepts such as software-defined networking, forwarding and control element separation, and network function virtualization. Notably, software-defined networking has attracted significant attention in telecommunication and data centers and thus already in some production-grade networks.
Despite the prevalence of software-defined networking in these domains, industrial networks are yet to see its benefits to encourage adoption. However, the misconceptions around the concept itself, the role of virtualization, and algorithms pose a significant obstacle.
Furthermore, the desire to accommodate new services in the automation industry results in a pattern of constantly increasing complexity of industrial networks, which is compounded by the requirement to provide stringent deterministic service guarantees considering characteristically different applications and thus posing a significant challenge for management, configuration, and maintenance as existing solutions are architecturally inflexible.
Therefore, the first contribution of this thesis addresses the misconceptions around software-defined networking by providing a comparative analysis of programmable network concepts, detailing where software-defined networks compare with other concepts and how its principles can be leveraged to evolve industrial networks.
Armed with the fundamental principles of programmable networks, the second contribution identifies virtualization technologies and proposes novel algorithms to provide varied quality of service guarantees on converged time-sensitive Ethernet networks using software-defined networking concepts.
Finally, a performance analysis of a software-defined hybrid deployment solution for control and management of time-sensitive Ethernet networks that integrates proposed novel algorithms is presented as an industrial use-case that enables industrial operators to harness the full potential of time-sensitive networks.
Das Aufzeichnen der Internetaktivität ist mit der Verknüpfung persönlicher Daten zu einer Schlüsselressource für viele kostenpflichtige und kostenfreie Dienste im Web geworden. Diese Dienste sind zum einen Webanwendungen, wie beispielsweise die von Google bereitgestellten Karten/Navigation oder Websuche, die täglich kostenlos verwendet werden. Zum anderen sind es alle Webseiten, die meist kostenlos Nachrichten oder allgemeine Informationen zu verschiedenen Themen bereitstellen. Durch das Aufrufen und die Nutzung dieser Webdienste werden alle Informationen, die im Webdienst verarbeitet werden, an den Dienstanbieter weitergeben. Dies umfasst nicht nur die im Benutzerkonto des Webdienstes gespeicherte Profildaten wie Name oder Adresse, sondern auch die Aktivität mit dem Webdienst wie das anklicken von Links oder die Verweildauer.
Darüber hinaus gibt es jedoch auch unzählige Drittparteien, welche zumeist im Hintergrund in die Webdienste eingebunden sind und das Benutzerverhalten der kompletten Webaktivität - Webseiten übergreifend - mitspeichern sowie auswerten. Der Einsatz verschiedener, in der Regel für den Benutzer verborgener Techniken, dient dazu das Online-Verhalten der Benutzer genau zu verfolgen und viele sensible Daten zu sammeln. Dieses Verhalten wird als Web-Tracking bezeichnet und wird hauptsächlich von Werbeunternehmen genutzt. Die gesammelten Daten sind oft personenbezogen und eine wertvolle Ressourcen der Unternehmen, um Beispielsweise passend zum Benutzerprofil personalisierte Werbung schalten zu können. Mit der Nutzung dieser personenbezogenen Daten entstehen aber auch weitreichendere Auswirkungen, welche sich unter anderem in Preisanpassungen für Benutzer mit speziellen Profilattributen, wie der Nutzung von teuren Endgeräten, widerspiegeln. Ziel dieser Arbeit ist es die Privatsphäre der Nutzer im Internet zu steigern und die Nutzerverfolgung von Web-Tracking signifikant zu reduzieren. Dabei stellen sich vier Herausforderungen, die jeweils einen Forschungsschwerpunkt dieser Arbeit bilden: (1) Systematische Analyse und Einordnung eingesetzter Tracking-Techniken, (2) Untersuchung vorhandener Schutzmechanismen und deren Schwachstellen,(3) Konzeption einer Referenzarchitektur zum Schutz vor Web-Tracking und (4) Entwurf einer automatisierten Testumgebungen unter Realbedingungen, um die Reduzierung von Web-Tracking in den entwickelten Schutzmaßnahmen zu untersuchen. Jeder dieser Forschungsschwerpunkte stellt neue Beiträge bereit, um einheitlich das übergeordnete Ziel zu erreichen: der Entwicklung von Schutzmaßnahmen gegen die Preisgabe sensibler Benutzerdaten im Internet. Der erste wissenschaftliche Beitrag dieser Dissertation ist eine umfassende Evaluation eingesetzter Web-Tracking Techniken und Methoden, sowie deren Gefahren, Risiken und Implikationen für die Privatsphäre der Internetnutzer. Die Evaluation beinhaltet zusätzlich die Untersuchung vorhandener Tracking-Schutzmechanismen und deren Schwachstellen. Die gewonnenen Erkenntnisse sind maßgeblich für die in dieser Arbeit neu entwickelten Ansätze und verbessern den bisherigen nicht hinreichend gewährleisteten Schutz vor Web-Tracking. Der zweite wissenschaftliche Beitrag ist die Entwicklung einer robusten Klassifizierung von Web-Tracking, der Entwurf einer effizienten Architektur zur Langzeituntersuchung von Web-Tracking sowie einer interaktiven Visualisierung des Auftreten von Web-Tracking im Internet. Dabei basiert der neue Klassifizierungsansatz, um Tracking zu identifizieren, auf der Entropie Messung des Informationsgehalts von Cookies. Die Resultate der Web-Tracking Langzeitstudien sind unter anderem 1.209 identifizierte Tracking-Domains auf den meistbesuchten Webseiten in Deutschland. Hierbei wurden innerhalb der Top 25 Webseiten im Durchschnitt 45 Tracking-Elemente pro Webseite gefunden. Der Tracker mit dem höchsten Potenzial zum Erstellen eines Benutzerprofils war doubleclick.com, da er 90% der Webseiten überwacht. Die Auswertung des untersuchten Tracking-Netzwerks ergab weiterhin einen detaillierten Einblick in die Tracking-Technik mithilfe von Weiterleitungslinks. Dabei haben wir 1,2 Millionen HTTP-Traces von monatelangen Crawls der 50.000 international meistbesuchten Webseiten analysiert. Die Ergebnisse zeigen, dass 11,6% dieser Webseiten HTTP-Redirects, verborgen in Webseiten-Links, zum Tracken verwenden. Dies wird eingesetzt, um den Webseitenverlauf des Benutzers nach dem Klick durch eine Kette von (Tracking-)Servern umzuleiten, welche in der Regel nicht sichtbar sind, bevor das beabsichtigte Link-Ziel geladen wird. In diesem Szenario erfasst der Tracker wertvolle Verbindungs-Metadaten zu Inhalt, Thema oder Benutzerinteressen der Website. Die Visualisierung des Tracking Ökosystem stellen wir in einem interaktiven Open-Source Web-Tool bereit. Der dritte wissenschaftliche Beitrag dieser Dissertation ist die Konzeption von zwei neuartigen Schutzmechanismen gegen Web-Tracking und der Aufbau einer automatisierten Simulationsumgebung unter Realbedingungen, um die Effektivität der Umsetzungen zu verifizieren. Der Fokus liegt auf den beiden meist verwendeten Tracking-Verfahren: Cookies (hierbei wird eine eindeutigen ID auf dem Gerät des Benutzers gespeichert), sowie Browser-Fingerprinting. Letzteres beschreibt eine Methode zum Sammeln einer Vielzahl an Geräteeigenschaften, um den Benutzer eindeutig zu (re- )identifizieren, ohne eine eindeutige ID auf dem Gerät zu speichern. Um die Effektivität der in dieser Arbeit entwickelten Schutzmechanismen vor Web-Tracking zu untersuchen, implementierten und evaluierten wir die Schutzkonzepte direkt im Chromium Browser. Das Ergebnis zeigt eine erfolgreiche Reduzierung von Web-Tracking um 44%. Zusätzlich verbessert das in dieser Arbeit entwickelte Konzept “Site Isolation” den Datenschutz des privaten Browsing-Modus, ermöglicht das Setzen eines manuellen Speicher-Zeitlimits von Cookies und schützt den Browser gegen verschiedene Bedrohungen wie CSRF (Cross-Site Request Forgery) oder CORS (Cross-Origin Ressource Sharing). Site Isolation speichert dabei den Status der lokalen Website in separaten Containern und kann dadurch diverse Tracking-Methoden wie Cookies, lokalStorage oder redirect tracking verhindern. Bei der Auswertung von 1,6 Millionen Webseiten haben wir gezeigt, dass der Tracker doubleclick.com das höchste Potenzial besitzt, den Nutzer zu verfolgen und auf 25% der 40.000 international meistbesuchten Webseiten vertreten ist. Schließlich demonstrieren wir in unserem erweiterten Chromium-Browser einen robusten Browser-Fingerprinting-Schutz. Der Test unseres Prototyps mittels 70.000 Browsersitzungen zeigt, dass unser Browser den Nutzer vor sogenanntem Browser-Fingerprinting Tracking schützt. Im Vergleich zu fünf anderen Browser-Fingerprint-Tools erzielte unser Prototyp die besten Ergebnisse und ist der erste Schutzmechanismus gegen Flash sowie Canvas Fingerprinting.
With the frequency and impact of data breaches raising, it has become essential for organizations to automate intrusion detection via machine learning solutions. This generally comes with numerous challenges, among others high class imbalance, changing target concepts and difficulties to conduct sound evaluation. In this thesis, we adopt a user-centered anomaly detection perspective to address selected challenges of intrusion detection, through a real-world use case in the identity and access management (IAM) domain. In addition to the previous challenges, salient properties of this particular problem are high relevance of categorical data, limited feature availability and total absence of ground truth.
First, we ask how to apply anomaly detection to IAM audit logs containing a restricted set of mixed (i.e. numeric and categorical) attributes. Then, we inquire how anomalous user behavior can be separated from normality, and this separation evaluated without ground truth. Finally, we examine how the lack of audit data can be alleviated in two complementary settings. On the one hand, we ask how to cope with users without relevant activity history ("cold start" problem). On the other hand, we seek how to extend audit data collection with heterogeneous attributes (i.e. categorical, graph and text) to improve insider threat detection.
After aggregating IAM audit data into sessions, we introduce and compare general anomaly detection methods for mixed data to a user identification approach, designed to learn the distinction between normal and malicious user behavior. We find that user identification outperforms general anomaly detection and is effective against masquerades. An additional clustering step allows to reduce false positives among similar users. However, user identification is not effective against insider threats. Furthermore, results suggest that the current scope of our audit data collection should be extended.
In order to tackle the "cold start" problem, we adopt a zero-shot learning approach. Focusing on the CERT insider threat use case, we extend an intrusion detection system by integrating user relations to organizational entities (like assignments to projects or teams) in order to better estimate user behavior and improve intrusion detection performance. Results show that this approach is effective in two realistic scenarios.
Finally, to support additional sources of audit data for insider threat detection, we propose a method representing audit events as graph edges with heterogeneous attributes. By performing detection at fine-grained level, this approach advantageously improves anomaly traceability while reducing the need for aggregation and feature engineering. Our results show that this method is effective to find intrusions in authentication and email logs.
Overall, our work suggests that masquerades and insider threats call for different detection methods. For masquerades, user identification is a promising approach. To find malicious insiders, graph features representing user context and relations to other entities can be informative. This opens the door for tighter coupling of intrusion detection with user identities, roles and privileges used in IAM solutions.
The current movement towards a smart grid serves as a solution to present power grid challenges by introducing numerous monitoring and communication technologies. A dependable, yet timely exchange of data is on the one hand an existential prerequisite to enable Advanced Metering Infrastructure (AMI) services, yet on the other a challenging endeavor, because the increasing complexity of the grid fostered by the combination of Information and Communications Technology (ICT) and utility networks inherently leads to dependability challenges.
To be able to counter this dependability degradation, current approaches based on high-reliability hardware or physical redundancy are no longer feasible, as they lead to increased hardware costs or maintenance, if not both. The flexibility of these approaches regarding vendor and regulatory interoperability is also limited. However, a suitable solution to the AMI dependability challenges is also required to maintain certain regulatory-set performance and Quality of Service (QoS) levels.
While a part of the challenge is the introduction of ICT into the power grid, it also serves as part of the solution. In this thesis a Network Functions Virtualization (NFV) based approach is proposed, which employs virtualized ICT components serving as a replacement for physical devices. By using virtualization techniques, it is possible to enhance the performability in contrast to hardware based solutions through the usage of virtual replacements of processes that would otherwise require dedicated hardware. This approach offers higher flexibility compared to hardware redundancy, as a broad variety of virtual components can be spawned, adapted and replaced in a short time. Also, as no additional hardware is necessary, the incurred costs decrease significantly. In addition to that, most of the virtualized components are deployed on Commercial-Off-The-Shelf (COTS) hardware solutions, further increasing the monetary benefit.
The approach is developed by first reviewing currently suggested solutions for AMIs and related services. Using this information, virtualization technologies are investigated for their performance influences, before a virtualized service infrastructure is devised, which replaces selected components by virtualized counterparts. Next, a novel model, which allows the separation of services and hosting substrates is developed, allowing the introduction of virtualization technologies to abstract from the underlying architecture. Third, the performability as well as monetary savings are investigated by evaluating the developed approach in several scenarios using analytical and simulative model analysis as well as proof-of-concept approaches. Last, the practical applicability and possible regulatory challenges of the approach are identified and discussed.
Results confirm that—under certain assumptions—the developed virtualized AMI is superior to the currently suggested architecture. The availability of services can be severely increased and network delays can be minimized through centralized hosting. The availability can be increased from 96.82% to 98.66% in the given scenarios, while decreasing the costs by over 60% in comparison to the currently suggested AMI architecture. Lastly, the performability analysis of a virtualized service prototype employing performance analysis and a Musa-Okumoto approach reveals that the AMI requirements are fulfilled.
Computer vision aims at developing algorithms to extract high-level information from images and videos. In the industry, for instance, such algorithms are applied to guide manufacturing robots, to visually monitor plants, or to assist human operators in recognizing specific components. Recent progress in computer vision has been dominated by deep artificial neural network, i.e., machine learning methods simulating the way that information flows in our biological brains, and the way that our neural networks adapt and learn from experience. For these methods to learn how to accurately perform complex visual tasks, large amounts of annotated images are needed. Collecting and labeling such domain-relevant training datasets is, however, a tedious—sometimes impossible—task. Therefore, it has become common practice to leverage pre-available three-dimensional (3D) models instead, to generate synthetic images for the recognition algorithms to be trained on. However, methods optimized over synthetic data usually suffer a significant performance drop when applied to real target images. This is due to the realism gap, i.e., the discrepancies between synthetic and real images (in terms of noise, clutter, etc.). In my work, three main directions were explored to bridge this gap.
First, an innovative end-to-end framework is proposed to render realistic depth images from 3D models, as a growing number of solutions (especially in the industry) are utilizing low-cost depth cameras (e.g., Microsoft Kinect and Intel RealSense) for recognition tasks. Based on a thorough study of these devices and the different types of noise impairing them, the proposed framework simulates their inner mechanisms, comprehensively modeling vital factors such as sensor noise, material reflectance, surface geometry, etc. Able to simulate a wide panel of depth sensors and to quickly generate large datasets, this framework is used to train algorithms for various recognition tasks, consistently and significantly enhancing their performance compared to other state-of-the-art simulation tools.
In some cases, however, relevant 2D or 3D object representations to generate synthetic samples are not available. Considering this different case of data scarcity, a solution is then proposed to incrementally build a representation of visual scenes from partial observations. Provided observations are localized from one to another based on their content and registered in a global memory with spatial properties. Simultaneously, this memory can be queried to render novel views of the scene. Furthermore, unobserved regions can be hallucinated in memory, in consistence with previous observations, hallucinations, and global priors. The efficacy of the proposed mnemonic and generative system, trainable end-to-end, is demonstrated on various 2D and 3D use-cases.
Finally, an advanced convolutional neural network pipeline is introduced, tackling the realism gap from a novel angle. While most methods addressing this problem focus on bringing synthetic samples—or the knowledge acquired from them—closer to the real target domain, the proposed solution performs the opposite process, mapping unseen target images into controlled synthetic domains. The pre-processed samples can then be handed to downstream recognition methods, themselves purely trained on similar synthetic data, to greatly improve their accuracy.
For each approach, a variety of qualitative and quantitative studies are detailed, providing successful comparisons to state-of-the-art methods. By proposing solutions to bridge the realism gap from either side, as well as a pipeline to improve the acquisition and generation of new visual content, this thesis provides a unique perspective on the challenges of data scarcity when building robust recognition systems.
A plethora of resources made available via retrieval systems in digital libraries remains untapped in the so called long tail of the Web. These long-tail websites get considerably less visits than major Web hubs.
Zero-effort queries ease the discovery of long-tail resources by proactively retrieving and presenting information based on a user’s context. However, zero-effort queries over existing digital library structures are challenging, since the underlying retrieval system is only accessible via an API. The information need must be expressed by a query, instead of optimizing the ranking between context and resources in the retrieval system directly. We address three research questions that arise from replacing the user information seeking process by zero-effort queries.
Our first question addresses the transformation of a user query to an automatic query, derived from the context. We present means to 1) identify the relevant context on different levels of granularity, 2) derive an information need from the context via keyword extraction and personalization and 3) express this information need in a query scheme that avoids over- or under-specified queries. We address the cold start problem with an approach to bootstrap user profiles from social media, even for passive users.
With the second question, we address the presentation of resources in zero-effort query scenarios, presenting guidelines for presentation interfaces in the browser and a visualization of the triadic relationship between context, query and results. QueryCrumbs, a compact query history visualization supports recalling information found in the past and exploratory search by visualizing qualitative and quantitative query similarity.
Our last question addresses the gap between (simple) keyword queries and the representation of resources by rich and complex meta-data. We investigate and extend feature representation learning techniques centered around the skip-gram model with negative sampling. Finally, we present an approach to learn representations from network and text jointly that can cope with the partial absence of one modality.
Experimental results show close to human performance of our zero-effort query and user profile generation approach and visualizations to be helpful in terms of transparency, efficiency and support for exploratory search. These results indicate that the proposed zero-effort query approach indeed eases the discovery of long-tail resources and the accompanying visualizations further facilitate this process. The joint representation model provides a first step to bridge the gap between query and resource representation and we plan to follow and investigate this route further in the future.
Whenever software faults can endanger human life, property, or the environment, the absence of faults must be ensured with utmost care and the best technologies available. Evidence is needed showing that all requirements are satisfied and that the risk of faults is reduced. One technique to conduct such a verification task—composed of the software to verify, the specification to check, and a model of the environment—is software model checking.
To conduct a verification task with a model checker, different models of the task are constructed. We distinguish between two types of task models: syntactic task models and semantic task models, which define the respective syntactic structure (control flow) and semantic structure (state transitions, invariants) of the verification task. When constructing such models, we can observe that similar structures and substructures reappear within and among different verification tasks. For example, the same assertions to check can appear in different functions, or the same predicate can be part of different invariants to describe sets of program states. Similarities that appear during the model construction process can be the result of solving similar reasoning problems, often solved using computationally expensive procedures (as typical for model checking), over and over again. Not reusing results of solving similar problems, not having a means for conducting repeated efforts automatically, or not trying to reduce the number of similar reasoning efforts, is a waste of precious resources.
To address these problems, we present a common conceptual and technical foundation for sharing syntactic and semantic task artifacts for reuse, within and among verification runs. Both the syntactic construction of a verification task and the construction of its semantic model—which describes all possible behaviors and states—are covered. We study how commonalities and regularities in the task models can be taken into account to facilitate the process of sharing task artifacts for reuse, and to make the overall verification process more efficient and effective. We introduce abstract transducers as the theoretical foundation of this thesis: a type of finite-state transducers with an inherent notion of abstraction for states, the input alphabet, and its output alphabet. Abstracting these transducers allows us to widen both the set of input words for that they produce output and the sets of output words. Abstract transducers are instantiated as task artifact transducers to map from program structures to task artifacts to share. We show that the notion of abstraction provides a means for increasing the scope for that task artifacts are shared for reuse. We present two instances of task artifact transducers: Yarn transducers and precision transducers. We use Yarn transducers for providing code to weave into the control-flow structure of a computer program, and present the Loom analysis as a means for orchestrating the weaving process. Precision transducers provide a means for sharing abstraction precisions for reuse, thus aid in defining the level of abstraction of a semantic task model. For both types of transducers, we provide empirical evidence on their practical applicability, for example, to verify Linux kernel modules, and show that they can help in increasing the verification performance.