The Drivers of Entrepreneurial Intentions - An Empirical Study among Information Systems and Computer Science Students

The last decade has seen an enormous increase in research on entrepreneurship education. However, there is so far only little research on entrepreneurship education in the field of information technology. To address this research gap, we conducted an empirical study based on an extended model of the Theory of Planned Behavior among Information Systems and Computer Science students. We found Attitude being the main driver for Information Systems students, and having discovered a Business Idea being the most influential factor for Computer Science students. In a more detailed analysis, the perception that being an entrepreneur does not come with a high risk to fail, the opportunity for self-fulfillment, and the chance of a high monetary reward could be identified as the crucial drivers regarding Information Systems students. Based on our findings, we discuss the implications for developing more entrepreneurially-oriented courses tailored to both groups of students

Lightweight, semi-automatic variability extraction: a case study on scientific computing

In scientific computing, researchers often use feature-rich software frameworks to simulate physical, chemical, and biological processes. Commonly, researchers follow a clone-and-own approach: Copying the code of an existing, similar simulation and adapting it to the new simulation scenario. In this process, a user has to select suitable artifacts (e.g., classes) from the given framework and replaces the existing artifacts from the cloned simulation. This manual process incurs substantial effort and cost as scientific frameworks are complex and provide large numbers of artifacts. To support researchers in this area, we propose a lightweight API-based analysis approach, called VORM, that recommends appropriate artifacts as possible alternatives for replacing given artifacts. Such alternative artifacts can speed up performance of the simulation or make it amenable to other use cases, without modifying the overall structure of the simulation. We evaluate the practicality of VORM—especially, as it is very lightweight but possibly imprecise—by means of a case study on the DUNE numerics framework and two simulations from the realm of physical simulations. Specifically, we compare the recommendations by VORM with recommendations by a domain expert (a developer of DUNE). VORM recommended 34 out of the 37 artifacts proposed by the expert. In addition, it recommended 2 artifacts that are applicable but have been missed by the expert and 32 artifacts not recommended by the expert, which however are still applicable in the simulation scenario with slight modifications. Diving deeper into the results, we identified an undiscovered bug and an inconsistency in DUNE, which corroborates the usefulness of VORM

Proteomics of spatially identified tissues in whole organs

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of biological specimens imaged in 3D as a whole are lacking. Here, we present DISCO-MS, a technology combining whole-organ imaging, deep learning-based image analysis, and ultra-high sensitivity mass spectrometry. DISCO-MS yielded qualitative and quantitative proteomics data indistinguishable from uncleared samples in both rodent and human tissues. Using DISCO-MS, we investigated microglia activation locally along axonal tracts after brain injury and revealed known and novel biomarkers. Furthermore, we identified initial individual amyloid-beta plaques in the brains of a young familial Alzheimer’s disease mouse model, characterized the core proteome of these aggregates, and highlighted their compositional heterogeneity. Thus, DISCO-MS enables quantitative, unbiased proteome analysis of target tissues following unbiased imaging of entire organs, providing new diagnostic and therapeutic opportunities for complex diseases, including neurodegeneration

Black-box performance modeling of configurable software systems

Software systems have become an important part of our daily lives, and a multitude of different application scenarios, user requirements, and hardware requirements have emerged. To handle these different requirements, most software systems offer some degree of configurability in terms of configuration options, allowing the user to adapt the software system to functional and non-functional requirements. Among non-functional requirements, the performance of the software system plays an important role to end-users. It is often unclear which configuration options influence the performance of the system. Specifically, there is a gap in how to select configurations affecting the system's performance when no previous knowledge is available. Furthermore, little is known about how the influence of configuration options on the system's performance changes across different workloads and software evolution. To bridge this gap, performance modeling based on statistical learning has proved useful. In this thesis, we follow three objectives in which we use or improve performance modeling of configurable software systems by statistical learning. First, we propose a novel sampling strategy, distance-based sampling, to improve the configuration selection (i.e., \emph{sampling}) while also addressing the shortcomings of existing state-of-the art sampling strategies. To assess the advantages and limitations of distance-based sampling, we compare it to state-of-the-art sampling strategies on multiple real-world configurable software systems. Our results indicate that distance-based sampling outperforms other state-of-the-art sampling strategies in terms of accuracy, but also suggest that there is still room for improvement with regard to scalability. Second, to assess how the influence of configuration options on the system's performance changes during software evolution, we use performance modeling on multiple real-world configurable software systems. This investigation delivers multiple valuable insights into the frequency of performance changes with implications to other research domains. We further investigate in how many cases the performance changes are documented by developers and find indications in which cases performance regressions are documented and when they are not. Third, besides configurability and software evolution, we also assess the role of workload variability in an exploratory study of the configurable software system \textsc{FastDownward}. For this purpose, we propose a performance modeling approach to identify performance changes considering workload variability and investigate the accuracy of this approach by evaluating precision and recall. Our results show that our approach is able to identify most performance changes, but we also identify the limitations of our approach, leaving room for further improvement. Furthermore, our performance measurements proved helpful in that they enabled us to discover and report multiple performance regressions in a real-world configurable software system. Overall, we contribute to performance modeling of configurable software systems by (1) proposing a novel sampling strategy designed to cover configuration spaces with regards to performance; (2) lifting how performance changes affect software configurability in practice and show implications on other research areas; (3) demonstrating how performance modeling can be used to find performance changes while including workload variability. To the best of our knowledge, we are the first to investigate configurability, evolution, and workload variability of configurable software systems together.Softwaresysteme sind zu einem wichtigen Bestandteil unseres täglichen Lebens geworden. Durch die Vielfalt der existierenden Softwaresysteme ist eine Vielzahl unterschiedlicher Anwendungsszenarien, Benutzeranforderungen sowie Hardwareanforderungen entstanden. Um diesen unterschiedlichen Anforderungen gerecht zu werden, bieten die meisten Softwaresysteme einen gewissen Grad an Konfigurierbarkeit, der es dem Benutzer ermöglicht, das Softwaresystem an funktionale und nicht-funktionale Anforderungen anzupassen. Unter den nicht-funktionalen Anforderungen spielt die Performance des Softwaresystems eine wichtige Rolle für die Endbenutzer. Häufig ist unklar, welche Konfigurationsoptionen die Performance des Systems beeinflussen. Aktuelle Verfahren zur Auswahl von Konfigurationen, die die Performance des Systems beeinflussen, weisen Schwachstellen auf. Darüber hinaus ist wenig darüber bekannt, wie sich die Auswirkungen von Konfigurationsoptionen auf die Performance im Laufe der Softwareentwicklung und bei unterschiedlichen Arbeitslasten ändern. Zur Lösung dieser Probleme kann die Performancemodellierung eingesetzt werden. In dieser Arbeit verfolgen wir drei Ziele, mit denen wir die Performancemodellierung von konfigurierbaren Softwaresystemen nutzen oder verbessern. Erstens stellen wir eine neuartige Sampling-Strategie vor, das distanzbasiertes Sampling. Damit kann die Auswahl der Konfigurationen (des Samplings) verbessert und gleichzeitig die Unzulänglichkeiten vermieden werden, die andere State-of-the-Art-Samplingstrategien aufweisen. Um die Vorzüge und Grenzen des distanzbasierten Samplings beurteilen zu können, vergleichen wir es mit modernsten Samplingstrategien auf mehreren realen, konfigurierbaren Softwaresystemen. Unsere Ergebnisse zeigen, dass distanzbasiertes Sampling andere moderne Samplingstrategien in Bezug auf die Genauigkeit übertrifft, aber auch, dass es in Bezug auf die Skalierbarkeit noch Raum für Verbesserungen gibt. Zweitens: Um zu beurteilen, wie sich der Einfluss von Konfigurationsoptionen auf die Performance des Systems über die Evolution der Software hinweg verändert, verwenden wir Performancemodellierung für mehrere reale konfigurierbare Softwaresysteme. Diese Untersuchung liefert wertvolle Einblicke in die Häufigkeit von Performanceänderungen mit Auswirkungen auf andere Forschungsbereiche. Außerdem untersuchen wir, wie häufig die Performanceänderungen von den Entwicklern dokumentiert werden und finden Hinweise darauf, in welchen Fällen Performanceänderungen dokumentiert werden und wann nicht. Drittens untersuchen wir in einer explorativen Studie zum konfigurierbaren Softwaresystem FastDownward neben der Konfigurierbarkeit und der Softwareevolution auch die Rolle der Arbeitslastvariabilität. Daher schlagen wir einen Ansatz zur Performancemodellierung vor, um Performanceänderungen unter Berücksichtigung der Arbeitslastvariabilität zu identifizieren, und untersuchen die Genauigkeit dieses Ansatzes, indem wir die Präzision und die Ausbeute auswerten. Unsere Ergebnisse zeigen, dass unser Ansatz in der Lage ist, die meisten Performanceänderungen zu erkennen. Wir zeigen aber auch die Grenzen unseres Ansatzes auf, die zeigen, wo noch weitere Verbesserungen möglich sind. Darüber hinaus haben sich unsere Performancemessungen als hilfreich erwiesen, da sie es uns ermöglichten, mehrere Performanceregressionen in einem realen konfigurierbaren Softwaresystem aufzudecken und an die Entwickler zu melden. Insgesamt leisten wir einen Beitrag zur Performancemodellierung konfigurierbarer Softwaresysteme, indem wir (1) eine neuartige Samplingstrategie vorschlagen, die darauf ausgelegt ist, Konfigurationsräume in Bezug auf die Leistung abzudecken; (2) aufzeigen, wie sich Performanceänderungen auf die Konfigurierbarkeit von Software in der Praxis auswirken und Auswirkungen auf andere Forschungsbereiche aufzeigen; (3) demonstrieren, wie die Performancemodellierung verwendet werden kann, um Performanceänderungen aufzudecken und zusammen mit der Variabilität der Arbeitslast zu berücksichtigen. Soweit uns bekannt ist, sind wir die Ersten, die Konfigurierbarkeit, Evolution und Arbeitslastvariabilität von konfigurierbaren Softwaresystemen gleichzeitig untersuchen

Spatial proteomics in three-dimensional intact specimens

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. VIDEO ABSTRACT