Presence is often considered the most important quale describing the subjective feeling of being in a computer-generated and/or computer-mediated virtual environment. The identification and separation of orthogonal presence components, i.e., the place illusion and the plausibility illusion, has been an accepted theoretical model describing Virtual Reality (VR) experiences for some time. This perspective article challenges this presence-oriented VR theory. First, we argue that a place illusion cannot be the major construct to describe the much wider scope of virtual, augmented, and mixed reality (VR, AR, MR: or XR for short). Second, we argue that there is no plausibility illusion but merely plausibility, and we derive the place illusion caused by the congruent and plausible generation of spatial cues and similarly for all the current model’s so-defined illusions. Finally, we propose congruence and plausibility to become the central essential conditions in a novel theoretical model describing XR experiences and effects.

Latency is an inherent problem of computing systems. Each computation takes time until the result is available. Virtual reality systems use elaborated computer resources to create virtual experiences. The latency of those systems is often ignored or assumed as small enough to provide a good experience. This cumulative thesis is comprised of published peer reviewed research papers exploring the behaviour and effects of latency. Contrary to the common description of time invariant latency, latency is shown to fluctuate. Few other researchers have looked into this time variant behaviour. This thesis explores time variant latency with a focus on randomly occurring latency spikes. Latency spikes are observed both for small algorithms and as end to end latency in complete virtual reality systems. Most latency measurements gather close to the mean latency with potentially multiple smaller clusters of larger latency values and rare extreme outliers. The latency behaviour differs for different implementations of an algorithm. Operating system schedulers and programming language environments such as garbage collectors contribute to the overall latency behaviour. The thesis demonstrates these influences on the example of different implementations of message passing. The plethora of latency sources result in an unpredictable latency behaviour. Measuring and reporting it in scientific experiments is important. This thesis describes established approaches to measuring latency and proposes an enhanced setup to gather detailed information. The thesis proposes to dissect the measured data with a stacked z-outlier-test to separate the clusters of latency measurements for better reporting. Latency in virtual reality applications can degrade the experience in multiple ways. The thesis focuses on cybersickness as a major detrimental effect. An approach to simulate time variant latency is proposed to make latency available as an independent variable in experiments to understand latency's effects. An experiment with modified latency shows that latency spikes can contribute to cybersickness. A review of related research shows that different time invariant latency behaviour also contributes to cybersickness.

Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario.

An enduring engineering problem is the creation of unreliable software leading to unreliable systems. One reason for this is source code is written in a complicated manner making it too hard for humans to review and understand. Complicated code leads to other issues beyond dependability, such as expanded development efforts and ongoing difficulties with maintenance, ultimately costing developers and users more money. There are many ideas regarding where blame lies in the reation of buggy and unreliable systems. One prevalent idea is the selected life cycle model is to blame. The oft-maligned “waterfall” life cycle model is a particularly popular recipient of blame. In response, many organizations changed their life cycle model in hopes of addressing these issues. Agile life cycle models have become very popular, and they promote communication between team members and end users. In theory, this communication leads to fewer misunderstandings and should lead to less complicated and more reliable code. Changing the life cycle model can indeed address communications ssues, which can resolve many problems with understanding requirements. However, most life cycle models do not specifically address coding practices or software architecture. Since lifecycle models do not address the structure of the code, they are often ineffective at addressing problems related to code complicacy. This dissertation answers several research questions concerning software complicacy, beginning with an investigation of traditional metrics and static analysis to evaluate their usefulness as measurement tools. This dissertation also establishes a new concept in applied linguistics by creating a measurement of software complicacy based on linguistic economy. Linguistic economy describes the efficiencies of speech, and this thesis shows the applicability of linguistic economy to software. Embedded in each topic is a discussion of the ramifications of overly complicated software, including the relationship of complicacy to software faults. Image recognition using machine learning is also investigated as a potential method of identifying problematic source code. The central part of the work focuses on analyzing the source code of hundreds of different projects from different areas. A static analysis was performed on the source code of each project, and traditional software metrics were calculated. Programs were also analyzed using techniques developed by linguists to measure expression and statement complicacy and identifier complicacy. Professional software engineers were also directly surveyed to understand mainstream perspectives. This work shows it is possible to use traditional metrics as indicators of potential project bugginess. This work also discovered it is possible to use image recognition to identify problematic pieces of source code. Finally, this work discovered it is possible to use linguistic methods to determine which statements and expressions are least desirable and more complicated for programmers. This work’s principle conclusion is that there are multiple ways to discover traits indicating a project or a piece of source code has characteristics of being buggy. Traditional metrics and static analysis can be used to gain some understanding of software complicacy and bugginess potential. Linguistic economy demonstrates a new tool for measuring software complicacy, and machine learning can predict where bugs may lie in source code. The significant implication of this work is developers can recognize when a project is becoming buggy and take practical steps to avoid creating buggy projects.

In scientific research, the independent reproduction of experiments is the source of trust. Detailed documentation is required to enable experiment reproduction. Reproducibility awards were created to honor the increased documentation effort. In this work, we propose a novel approach toward reproducible research—a structured experimental workflow that allows the creation of reproducible experiments without requiring additional efforts of the researcher. Moreover, we present our own testbed and toolchain, namely, plain orchestrating service (pos), which enables the creation of such experimental workflows. The experiment is documented by our proposed, fully scripted experiment structure. In addition, pos provides scripts enabling the automation of the bundling and release of all experimental artifacts. We provide an interactive environment where pos experiments can be executed and reproduced, available at https://gallenmu.github.io/single-server-experiment.