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This research investigates the efficacy of multisensory cues for locating targets in Augmented Reality (AR). Sensory constraints can impair perception and attention in AR, leading to reduced performance due to factors such as conflicting visual cues or a restricted field of view. To address these limitations, the research proposes head-based multisensory guidance methods that leverage audio-tactile cues to direct users' attention towards target locations. The research findings demonstrate that this approach can effectively reduce the influence of sensory constraints, resulting in improved search performance in AR. Additionally, the thesis discusses the limitations of the proposed methods and provides recommendations for future research.
Risikobasierte Authentifizierung (RBA) ist ein adaptiver Ansatz zur Stärkung der Passwortauthentifizierung. Er überwacht eine Reihe von Merkmalen, die sich auf das Loginverhalten während der Passworteingabe beziehen. Wenn sich die beobachteten Merkmalswerte signifikant von denen früherer Logins unterscheiden, fordert RBA zusätzliche Identitätsnachweise an. Regierungsbehörden und ein Erlass des US-Präsidenten empfehlen RBA, um Onlineaccounts vor Angriffen mit gestohlenen Passwörtern zu schützen. Trotz dieser Tatsachen litt RBA unter einem Mangel an offenem Wissen. Es gab nur wenige bis keine Untersuchungen über die Usability, Sicherheit und Privatsphäre von RBA. Das Verständnis dieser Aspekte ist jedoch wichtig für eine breite Akzeptanz.
Diese Arbeit soll ein umfassendes Verständnis von RBA mit einer Reihe von Studien vermitteln. Die Ergebnisse ermöglichen es, datenschutzfreundliche RBA-Lösungen zu schaffen, die die Authentifizierung stärken bei gleichzeitig hoher Menschenakzeptanz.
Intelligent virtual agents provide a framework for simulating more life-like behavior and increasing plausibility in virtual training environments. They can improve the learning process if they portray believable behavior that can also be controlled to support the training objectives. In the context of this thesis, cognitive agents are considered a subset of intelligent virtual agents (IVA) with the focus on emulating cognitive processes to achieve believable behavior. The complexity of employed algorithms, however, is often limited since multiple agents need to be simulated in real-time. Available solutions focus on a subset of the indicated aspects: plausibility, controllability, or real-time capability (scalability). Within this thesis project, an agent architecture for attentive cognitive agents is developed that considers all three aspects at once. The result is a lightweight cognitive agent architecture that is customizable to application-specific requirements. A generic trait-based personality model influences all cognitive processes, facilitating the generation of consistent and individual behavior. An additional mapping process provides a formalized mechanism to transfer results of psychological studies to the architecture. Personality profiles are combined with an emotion model to achieve situational behavior adaptation. Which action an agent selects in a situation also influences plausibility. An integral element of this selection process is an agent's knowledge about its world. Therefore, synthetic perception is modeled and integrated into the architecture to provide a credible knowledge base. The developed perception module includes a unified sensor interface, a memory hierarchy, and an attention process. With the presented realization of the architecture (CAARVE), it is possible for the first time to simulate cognitive agents, whose behaviors are simultaneously computable in real-time and controllable. The architecture's applicability is demonstrated by integrating an agent-based traffic simulation built with CAARVE into a bicycle simulator for road-safety education. The developed ideas and their realization are evaluated within this work using different strategies and scenarios. For example, it is shown how CAARVE agents utilize personality profiles and emotions to plausibly resolve deadlocks in traffic simulations. Controllability and adaptability are demonstrated in additional scenarios. Using the realization, 200 agents can be simulated in real-time (50 FPS), illustrating scalability. The achieved results verify that the developed architecture can generate plausible and controllable agent behavior in real-time. The presented concepts and realizations provide sound fundamentals to everyone interested in simulating IVA in real-time environments.
Skill generalisation and experience acquisition for predicting and avoiding execution failures
(2023)
For performing tasks in their target environments, autonomous robots usually execute and combine skills. Robot skills in general and learning-based skills in particular are usually designed so that flexible skill acquisition is possible, but without an explicit consideration of execution failures, the impact that failure analysis can have on the skill learning process, or the benefits of introspection for effective coexistence with humans. Particularly in human-centered environments, the ability to understand, explain, and appropriately react to failures can affect a robot's trustworthiness and, consequently, its overall acceptability. Thus, in this dissertation, we study the questions of how parameterised skills can be designed so that execution-level decisions are associated with semantic knowledge about the execution process, and how such knowledge can be utilised for avoiding and analysing execution failures. The first major segment of this work is dedicated to developing a representation for skill parameterisation whose objective is to improve the transparency of the skill parameterisation process and enable a semantic analysis of execution failures. We particularly develop a hybrid learning-based representation for parameterising skills, called an execution model, which combines qualitative success preconditions with a function that maps parameters to predicted execution success. The second major part of this work focuses on applications of the execution model representation to address different types of execution failures. We first present a diagnosis algorithm that, given parameters that have resulted in a failure, finds a failure hypothesis by searching for violations of the qualitative model, as well as an experience correction algorithm that uses the found hypothesis to identify parameters that are likely to correct the failure. Furthermore, we present an extension of execution models that allows multiple qualitative execution contexts to be considered so that context-specific execution failures can be avoided. Finally, to enable the avoidance of model generalisation failures, we propose an adaptive ontology-assisted strategy for execution model generalisation between object categories that aims to combine the benefits of model-based and data-driven methods; for this, information about category similarities as encoded in an ontology is integrated with outcomes of model generalisation attempts performed by a robot. The proposed methods are exemplified in terms of various use cases - object and handle grasping, object stowing, pulling, and hand-over - and evaluated in multiple experiments performed with a physical robot. The main contributions of this work include a formalisation of the skill parameterisation problem by considering execution failures as an integral part of the skill design and learning process, a demonstration of how a hybrid representation for parameterising skills can contribute towards improving the introspective properties of robot skills, as well as an extensive evaluation of the proposed methods in various experiments. We believe that this work constitutes a small first step towards more failure-aware robots that are suitable to be used in human-centered environments.
The processing of employee personal data is dramatically increasing. To protect employees' fundamental right to privacy, the law provides for the implementation of privacy controls, including transparency and intervention. At present, however, the stakeholders responsible for putting these obligations into action, such as employers and software engineers, simply lack the fundamental knowledge needed to design and implement the necessary controls. Indeed, privacy research has so far focused mainly on consumer relations in the private context. In contrast, privacy in the employment context is less well studied. However, since privacy is highly context-dependent, existing knowledge and privacy controls from other contexts cannot simply be adopted to the employment context. In particular, privacy in employment is subject to different legal and social norms, which require a different conceptualization of the right to privacy than is usual in other contexts. To adequately address these aspects, there is broad consensus that privacy must be regarded as a socio-technical concept in which human factors must be considered alongside technical-legal factors. Today, however, there is a particular lack of knowledge about human factors in employee privacy. Disregarding the needs and concerns of individuals or lack of usability, though, are common reasons for the failure of privacy and security measures in practice. This dissertation addresses key knowledge gaps on human factors in employee privacy by presenting the results of a total of three in-depth studies with employees in Germany. The results provide insights into employees' perceptions of the right to privacy, as well as their perceptions and expectations regarding the processing of employee personal data. The insights gained provide a foundation for the human-centered design and implementation of employee-centric privacy controls, i.e., privacy controls that incorporate the views, expectations, and capabilities of employees. Specifically, this dissertation presents the first mental models of employees on the right to informational self-determination, the German equivalent of the right to privacy. The results provide insights into employees' (1) perceptions of categories of data, (2) familiarity and expectations of the right to privacy, and (3) perceptions of data processing, data flow, safeguards, and threat models. In addition, three major types of mental models are presented, each with a different conceptualization of the right to privacy and a different desire for control. Moreover, this dissertation provides multiple insights into employees' perceptions of data sensitivity and willingness to disclose personal data in employment. Specifically, it highlights the uniqueness of the employment context compared to other contexts and breaks down the multi-dimensionality of employees' perceptions of personal data. As a result, the dimensions in which employees perceive data are presented, and differences among employees are highlighted. This is complemented by identifying personal characteristics and attitudes toward employers, as well as toward the right to privacy, that influence these perceptions. Furthermore, this dissertation provides insights into practical aspects for the implementation of personal data management solutions to safeguard employee privacy. Specifically, it presents the results of a user-centered design study with employees who process personal data of other employees as part of their job. Based on the results obtained, a privacy pattern is presented that harmonizes privacy obligations with personal data processing activities. The pattern is useful for designing privacy controls that help these employees handle employee personal data in a privacy-compliant manner, taking into account their skills and knowledge, thus helping to protect employee privacy. The outcome of this dissertation benefits a wide range of stakeholders who are involved in the protection of employee privacy. For example, it highlights the challenges to be considered by employers and software engineers when conceptualizing and designing employee-centric privacy controls. Policymakers and researchers gain a better understanding of employees' perceptions of privacy and obtain fundamental knowledge for future research into theoretical and abstract concepts or practical issues of employee privacy. Employers, IT engineers, and researchers gain insights into ways to empower data processing employees to handle employee personal data in a privacy-compliant manner, enabling employers to improve and promote compliance. Since the basic principles underlying informational self-determination have been incorporated into European privacy legislation, we are confident that our results are also of relevance to stakeholders outside Germany.
Collaboration among multiple users on large screens leads to complicated behavior patterns and group dynamics. To gain a deeper understanding of collaboration on vertical, large, high-resolution screens, this dissertation builds on previous research and gains novel insights through new observational studies. Among other things, the collected results reveal new patterns of collaborative coupling, suggest that territorial behavior is less critical than shown in previous research, and demonstrate that workspace awareness can also negatively affect the effectiveness of individual users.
In this thesis it is posed that the central object of preference discovery is a co-creative process in which the Other can be represented by a machine. It explores efficient methods to enhance introverted intuition using extraverted intuition's communication lines. Possible implementations of such processes are presented using novel algorithms that perform divergent search to feed the users' intuition with many examples of high quality solutions, allowing them to take influence interactively. The machine feeds and reflects upon human intuition, combining both what is possible and preferred. The machine model and the divergent optimization algorithms are the motor behind this co-creative process, in which machine and users co-create and interactively choose branches of an ad hoc hierarchical decomposition of the solution space.
The proposed co-creative process consists of several elements: a formal model for interactive co-creative processes, evolutionary divergent search, diversity and similarity, data-driven methods to discover diversity, limitations of artificial creative agents, matters of efficiency in behavioral and morphological modeling, visualization, a connection to prototype theory, and methods to allow users to influence artificial creative agents. This thesis helps putting the human back into the design loop in generative AI and optimization.
This thesis explores novel haptic user interfaces for touchscreens, virtual and remote environments (VE and RE). All feedback modalities have been designed to study performance and perception while focusing on integrating an additional sensory channel - the sense of touch. Related work has shown that tactile stimuli can increase performance and usability when interacting with a touchscreen. It was also shown that perceptual aspects in virtual environments could be improved by haptic feedback. Motivated by previous findings, this thesis examines the versatility of haptic feedback approaches. For this purpose, five haptic interfaces from two application areas are presented. Research methods from prototyping and experimental design are discussed and applied. These methods are used to create and evaluate the interfaces; therefore, seven experiments have been performed. All five prototypes use a unique feedback approach. While three haptic user interfaces designed for touchscreen interaction address the fingers, two interfaces developed for VE and RE target the feet. Within touchscreen interaction, an actuated touchscreen is presented, and study shows the limits and perceptibility of geometric shapes. The combination of elastic materials and a touchscreen is examined with the second interface. A psychophysical study has been conducted to highlight the potentials of the interface. The back of a smartphone is used for haptic feedback in the third prototype. Besides a psychophysical study, it is found that the touch accuracy could be increased. Interfaces presented in the second application area also highlight the versatility of haptic feedback. The sides of the feet are stimulated in the first prototype. They are used to provide proximity information of remote environments sensed by a telepresence robot. In a study, it was found that spatial awareness could be increased. Finally, the soles of the feet are stimulated. A designed foot platform that provides several feedback modalities shows that self-motion perception can be increased.
At the end of 2019, about 4.1 billion people on earth were using the internet. Because people entrust their most intimate and private data to their devices, the European legislation has declared the protection of natural persons in relation to the processing of personal data as a fundamental right. In 2018 23 million people worldwide, having the responsibility of implementing data security and privacy, were developing software. However, the implementation of data and application security is a challenge, as evidenced by over 41 thousand documented security incidents in 2019. Probably the most basic, powerful, and frequently used tools software developers work with are Application Programming Interfaces (APIs). Security APIs are essential tools to bring data and application security into software products. However, research results have revealed that usability problems of security APIs lead to insecure API use during development. Basic security requirements such as securely stored passwords, encrypted files or secure network connections can become an error-prone challenge and in consequence lead to unreliable or missing security and privacy. Because software developers hold a key position in the development processes of software, not properly operating security tools pose a risk to all people using software. However, little is known about the requirements of developers to address the problem and improve the usability of security APIs. This thesis is one of the first to examine the usability of security APIs. To this end, the author examines to what extent information flows can support software developers in using security APIs to implement secure software by conducting empirical studies with software developers. This thesis has contributed fundamental results that can be used in future work to identify and improve important information flows in software development. The studies have clearly shown that developer-tailored information flows with adapted security-relevant content have a positive influence on the correct implementation of security. However, the results have also led to the conclusion that API producers need to pay special attention to the channels through which they direct information flows to API users and how the information is designed to be useful for them. In many cases, it is not enough to provide security-relevant information via the documentation only. Here, proactive methods like the API security advice proposed by this thesis achieve significantly better results in terms of findability and actionable support. To further increase the effectiveness of the API security advice, this thesis developed a cryptographic API warning design for the terminal by adopting a participatory design approach with experienced software developers. However, it also became clear that a single information flow can only support up to a certain extent. As observed from two studies conducted in complex API environments in web development, multiple complementary information flows have to meet the extensive information needs of developers to be able to develop secure software. Some evaluated new approaches provided promising insights towards more API consumer-focused documentation designs as a complement to API warnings.
Despite their age, ray-based rendering methods are still a very active field of research with many challenges when it comes to interactive visualization. In this thesis, we present our work on Guided High-Quality Rendering, Foveated Ray Tracing for Head Mounted Displays and Hash-based Hierarchical Caching and Layered Filtering. Our system for Guided High-Quality Rendering allows for guiding the sampling rate of ray-based rendering methods by a user-specified Region of Interest (RoI). We propose two interaction methods for setting such an RoI when using a large display system and a desktop display, respectively. This makes it possible to compute images with a heterogeneous sample distribution across the image plane. Using such a non-uniform sample distribution, the rendering performance inside the RoI can be significantly improved in order to judge specific image features. However, a modified scheduling method is required to achieve sufficient performance. To solve this issue, we developed a scheduling method based on sparse matrix compression, which has shown significant improvements in our benchmarks. By filtering the sparsely sampled image appropriately, large brightness variations in areas outside the RoI are avoided and the overall image brightness is similar to the ground truth early in the rendering process. When using ray-based methods in a VR environment on head-mounted display de vices, it is crucial to provide sufficient frame rates in order to reduce motion sickness. This is a challenging task when moving through highly complex environments and the full image has to be rendered for each frame. With our foveated rendering sys tem, we provide a perception-based method for adjusting the sample density to the user’s gaze, measured with an eye tracker integrated into the HMD. In order to avoid disturbances through visual artifacts from low sampling rates, we introduce a reprojection-based rendering pipeline that allows for fast rendering and temporal accumulation of the sparsely placed samples. In our user study, we analyse the im pact our system has on visual quality. We then take a closer look at the recorded eye tracking data in order to determine tracking accuracy and connections between different fixation modes and perceived quality, leading to surprising insights. For previewing global illumination of a scene interactively by allowing for free scene exploration, we present a hash-based caching system. Building upon the concept of linkless octrees, which allow for constant-time queries of spatial data, our frame work is suited for rendering such previews of static scenes. Non-diffuse surfaces are supported by our hybrid reconstruction approach that allows for the visualization of view-dependent effects. In addition to our caching and reconstruction technique, we introduce a novel layered filtering framework, acting as a hybrid method between path space and image space filtering, that allows for the high-quality denoising of non-diffuse materials. Also, being designed as a framework instead of a concrete filtering method, it is possible to adapt most available denoising methods to our layered approach instead of relying only on the filtering of primary hitpoints.
With the digital transformation, software systems have become an integral part of our society and economy. In every part of our life, software systems are increasingly utilized to, e.g., simplify housework or to optimize business processes. All these applications are connected to the Internet, which already includes millions of software services consumed by billions of people. Applications which process such a magnitude of users and data traffic requires to be highly scalable and are therefore denoted as Ultra Large Scale (ULS) systems. Roy Fielding has defined one of the first approaches which allows designing modern ULS software systems. In his doctoral thesis, Fielding introduced the architectural style Representational State Transfer (REST) which builds the theoretical foundation of the web. At present, the web is considered as the world's largest ULS system. Due to a large number of users and the significance of software for society and the economy, the security of ULS systems is another crucial quality factor besides high scalability.
Optimization plays an essential role in industrial design, but is not limited to minimization of a simple function, such as cost or strength. These tools are also used in conceptual phases, to better understand what is possible. To support this exploration we focus on Quality Diversity (QD) algorithms, which produce sets of varied, high performing solutions. These techniques often require the evaluation of millions of solutions -- making them impractical in design cases. In this thesis we propose methods to radically improve the data-efficiency of QD with machine learning, enabling its application to design. In our first contribution, we develop a method of modeling the performance of evolved neural networks used for control and design. The structures of these networks grow and change, making them difficult to model -- but with a new method we are able to estimate their performance based on their heredity, improving data-efficiency by several times. In our second contribution we combine model-based optimization with MAP-Elites, a QD algorithm. A model of performance is created from known designs, and MAP-Elites creates a new set of designs using this approximation. A subset of these designs are the evaluated to improve the model, and the process repeats. We show that this approach improves the efficiency of MAP-Elites by orders of magnitude. Our third contribution integrates generative models into MAP-Elites to learn domain specific encodings. A variational autoencoder is trained on the solutions produced by MAP-Elites, capturing the common “recipe” for high performance. This learned encoding can then be reused by other algorithms for rapid optimization, including MAP-Elites. Throughout this thesis, though the focus of our vision is design, we examine applications in other fields, such as robotics. These advances are not exclusive to design, but serve as foundational work on the integration of QD and machine learning.
Evaluation and Optimization of IEEE802.11 multi-hop Backhaul Networks with Directional Antennas
(2020)
A major problem for rural areas is the inaccessibility to affordable broadband Internet connections. In these areas distances are large, and digging a cable into the ground is extremely expensive, considering the small number of potential customers at the end of that cable. This leads to a digital divide, where urban areas enjoy a high-quality service at low cost, while rural areas suffer from the reverse.
This work is dedicated to an alternative technical approach aiming to reduce the cost for Internet Service Provider in rural areas: WiFi-based Long Distance networks. A set of significant contributions of technology related aspects of WiFi-based Long Distance networks is described in three different fields: Propagation on long distance Wi-Fi links, MAC-layer scheduling and Interference modeling and Channel Assignment with directional antennas.
For each field, the author composes and discusses the state-of-the-art. Afterwards, the author derives research questions and tackles several open issues to develop these kinds of networks further towards a suitable technology for the backhaul segment.
Computer graphics research strives to synthesize images of a high visual realism that are indistinguishable from real visual experiences. While modern image synthesis approaches enable to create digital images of astonishing complexity and beauty, processing resources remain a limiting factor. Here, rendering efficiency is a central challenge involving a trade-off between visual fidelity and interactivity. For that reason, there is still a fundamental difference between the perception of the physical world and computer-generated imagery. At the same time, advances in display technologies drive the development of novel display devices. The dynamic range, the pixel densities, and refresh rates are constantly increasing. Display systems enable a larger visual field to be addressed by covering a wider field-of-view, due to either their size or in the form of head-mounted devices. Currently, research prototypes are ranging from stereo and multi-view systems, head-mounted devices with adaptable lenses, up to retinal projection, and lightfield/holographic displays. Computer graphics has to keep step with, as driving these devices presents us with immense challenges, most of which are currently unsolved. Fortunately, the human visual system has certain limitations, which means that providing the highest possible visual quality is not always necessary. Visual input passes through the eye’s optics, is filtered, and is processed at higher level structures in the brain. Knowledge of these processes helps to design novel rendering approaches that allow the creation of images at a higher quality and within a reduced time-frame. This thesis presents the state-of-the-art research and models that exploit the limitations of perception in order to increase visual quality but also to reduce workload alike - a concept we call perception-driven rendering. This research results in several practical rendering approaches that allow some of the fundamental challenges of computer graphics to be tackled. By using different tracking hardware, display systems, and head-mounted devices, we show the potential of each of the presented systems. The capturing of specific processes of the human visual system can be improved by combining multiple measurements using machine learning techniques. Different sampling, filtering, and reconstruction techniques aid the visual quality of the synthesized images. An in-depth evaluation of the presented systems including benchmarks, comparative examination with image metrics as well as user studies and experiments demonstrated that the methods introduced are visually superior or on the same qualitative level as ground truth, whilst having a significantly reduced computational complexity.
The initially large number of variants is reduced by applying custom variant annotation and filtering procedures. This requires complex software toolchains to be set up and data sources to be integrated. Furthermore, increasing study sizes subsequently require higher efforts to manage datasets in a multi-user and multi-institution environment. It is common practice to expect numerous iterations of continuative respecification and refinement of filter strategies, when the cause for a disease or phenotype is unknown. Data analysis support during this phase is fundamental, because handling the large volume of data is not possible or inadequate for users with limited computer literacy. Constant feedback and communication is necessary when filter parameters are adjusted or the study grows with additional samples. Consequently, variant filtering and interpretation becomes time-consuming and hinders a dynamic and explorative data analysis by experts.
In the field of domestic service robots, recovery from faults is crucial to promote user acceptance. In this context, this work focuses on some specific faults which arise from the interaction of a robot with its real world environment. Even a well-modelled robot may fail to perform its tasks successfully due to external faults which occur because of an infinite number of unforeseeable and unmodelled situations. Through investigating the most frequent failures in typical scenarios which have been observed in real-world demonstrations and competitions using the autonomous service robots Care-O-Bot III and youBot, we identified four different fault classes caused by disturbances, imperfect perception, inadequate planning operator or chaining of action sequences. This thesis then presents two approaches to handle external faults caused by insufficient knowledge about the preconditions of the planning operator. The first approach presents reasoning on detected external faults using knowledge about naive physics. The naive physics knowledge is represented by the physical properties of objects which are formalized in a logical framework. The proposed approach applies a qualitative version of physical laws to these properties in order to reason. By interpreting the reasoning results the robot identifies the information about the situations which can cause the fault. Applying this approach to simple manipulation tasks like picking and placing objects show that naive physics holds great possibilities for reasoning on unknown external faults in robotics. The second approach includes missing knowledge about the execution of an action through learning by experimentation. Firstly, it investigates such representation of execution specific knowledge that can be learned for one particular situation and reused for situations which deviate from the original. The combination of symbolic and geometric models allows us to represent action execution knowledge effectively. This representation is called action execution model (AEM) here. The approach provides a learning strategy which uses a physical simulation for generating the training data to learn both symbolic and geometric aspects of the model. The experimental analysis, performed on two physical robots, shows that AEM can reliably describe execution specific knowledge and thereby serving as a potential model for avoiding the occurrence of external faults.
Diese Arbeit beschäftigt sich mit der Effizienz der Seitenkanal-Kryptanalyse. In Teil II dieser Arbeit demonstrieren wir, wie die Laufzeit der wichtigsten Analysewerkzeuge mit Hilfe der CUDA Plattform erheblich gesteigert werden kann. Zweitens untersuchen wir neue Ansätze der profilierenden Seitenkanal-Kryptanalyse. Der Forschungszweig des maschinellen Lernens kann für deutliche Verbesserungen adaptiert werden, wurde jedoch wenig dahingehend untersucht. In Teil III dieser Arbeit präsentieren wir zwei neue Methoden, die einige Gemeinsamkeiten jedoch auch einige Unterschiede aufbieten, sodass sich Prüfergebnisse in einem vollständigeren Bild zeigen lassen. Darüber hinaus schlagen wir in Teil IV eine Seitenkanalanwendung zum Schutz geistigen Eigentums (IP) vor. In Teil V beschäftigen wir uns tiefergehend mit praktischer Seitenkanal-Kryptanalyse, indem wir Attacken auf einen Sicherheitsmikrokontroller durchführen, der Anwendung in einer, in Deutschland weit verbreiteten, EC Karte findet.
As robots are becoming ubiquitous and more capable, the need for introducing solid robot software development methods is pressing to increase robots' task spectrum. This thesis is concerned with improving software engineering of robot perception systems. The presented research employs a model-based approach to provide the means to represent knowledge about robotics software. The thesis is divided into three parts, namely research on the specification, deployment and adaptation of robot perception systems.
The knowledge of Software Features (SFs) is vital for software developers and requirements specialists during all software engineering phases: to understand and derive software requirements, to plan and prioritize implementation tasks, to update documentation, or to test whether the final product correctly implements the requested SF. In most software projects, SFs are managed in conjunction with other information such as bug reports, programming tasks, or refactoring tasks with the aid of Issue Tracking Systems (ITSs). Hence ITSs contains a variety of information that is only partly related to SFs. In practice, however, the usage of ITSs to store SFs comes with two major problems: (1) ITSs are neither designed nor used as documentation systems. Therefore, the data inside an ITS is often uncategorized and SF descriptions are concealed in rather lengthy. (2) Although an SF is often requested in a single sentence, related information can be scattered among many issues. E.g. implementation tasks related to an SF are often reported in additional issues. Hence, the detection of SFs in ITSs is complicated: a manual search for the SFs implies reading, understanding and exploiting the Natural Language (NL) in many issues in detail. This is cumbersome and labor intensive, especially if related information is spread over more than one issue. This thesis investigates whether SF detection can be supported automatically. First the problem is analyzed: (i) An empirical study shows that requests for important SFs reside in ITSs, making ITSs a good tar- get for SF detection. (ii) A second study identifies characteristics of the information and related NL in issues. These characteristics repre- sent opportunities as well as challenges for the automatic detection of SFs. Based on these problem studies, the Issue Tracking Software Feature Detection Method (ITSoFD), is proposed. The method has two main components and includes an approach to preprocess issues. Both components address one of the problems associated with storing SFs in ITSs. ITSoFD is validated in three solution studies: (I) An empirical study researches how NL that describes SFs can be detected with techniques from Natural Language Processing (NLP) and Machine Learning. Issues are parsed and different characteristics of the issue and its NL are extracted. These characteristics are used to clas- sify the issue’s content and identify SF description candidates, thereby approaching problem (1). (II) An empirical study researches how issues that carry information potentially related to an SF can be detected with techniques from NLP and Information Retrieval. Characteristics of the issue’s NL are utilized to create a traceability network vii of related issues, thereby approaching problem (2). (III) An empirical study researches how NL data in issues can be preprocessed using heuristics and hierarchical clustering. Code, stack traces, and other technical information is separated from NL. Heuristics are used to identify candidates for technical information and clustering improves the heuristic’s results. The technique can be applied to support components, I. and II.
Over the last 50 years, the controlled motion of robots has become a very mature domain of expertise. It can deal with all sorts of topologies and types of joints and actuators, with kinematic as well as dynamic models of devices, and with one or several tools or sensors attached to the mechanical structure. Nevertheless, the domain has not succeeded in standardizing the modelling of robot devices (including such fundamental entities as “reference frames”!), let alone the semantics of their motion specification and control. This thesis aims to solve this long-standing problem, from three different sides: semantic models for robot kinematics and dynamics, semantic models of all possible motion specification and control problems, and software that can support the latter while being configured by a systematic use of the former.