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In dieser Ausarbeitung wird eine zeitliche Vorhersage von Erdbeben getroffen. Hierfür werden mit einem Datensatz aus Labor-Erdbeben Convolutional Neural Networks (CNN) trainiert. Die trainierten Netzwerke geben Vorhersagen, indem sie einen Input an seismischen Daten klassifizieren. Durch das Klassifizieren kann das CNN die zeitliche Entfernung zum nächsten Erdbeben vorhersagen. Es werden hierfür zwei Ansätze miteinander verglichen. Beim ersten Ansatz werden die Originaldaten in ein CNN gegeben. Beim zweiten Ansatz wird vor dem CNN eine Vorverarbeitung der Daten mit den Mel Frequency Cepstral Coefficients (MFCC) durchgeführt. Es zeigt sich, dass mit beiden Ansätzen eine gute Klassifikation möglich ist. Die Kombination aus MFCC und CNN liefert die besseren quantitativen Ergebnisse. Hierbei konnte eine Genauigkeit von 65 % erreicht werden.
Context: Organizations are increasingly challenged by dynamic and technical market environments. Traditional product roadmapping practices such as detailed and fixed long-term planning typically fail in such environments. Therefore, companies are actively seeking ways to improve their product roadmapping approach. Goal: This paper aims at identifying problems and challenges with respect to product roadmapping. In addition, it aims at understanding how companies succeed in improving their roadmapping practices in their respective company contexts. The study focuses on mid-sized and large companies developing software-intensive products in dynamic and technical market environments. Method: We conducted semi structured expert interviews with 15 experts from 13 German companies and conducted a thematic data analysis. Results: The analysis showed that a significant number of companies is still struggling with traditional feature based product-roadmapping and opinion based prioritization of features. The most promising areas for improvement are stating the outcomes a company is trying to achieve and making them part of the roadmap, sharing or co-developing the roadmap with stakeholders, and the establishing discovery activities.
Among the multitude of software development processes available, hardly any is used by the book. Regardless of company size or industry sector, a majority of project teams and companies use customized processes that combine different development methods— so-called hybrid development methods. Even though such hybrid development methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this paper, we make a first step towards devising such guidelines. Grounded in 1,467 data points from a large-scale online survey among practitioners, we study the current state of practice in process use to answer the question: What are hybrid development methods made of? Our findings reveal that only eight methods and few practices build the core of modern software development. This small set allows for statistically constructing hybrid development methods. Using an 85% agreement level in the participants’ selections, we provide two examples illustrating how hybrid development methods are characterized by the practices they are made of. Our evidence-based analysis approach lays the foundation for devising hybrid development methods.
In this paper we describe an interactive web-based visual analysis tool for Formula one races. It first provides an overview about all races on a yearly basis in a calendar-like representation. From this starting point, races can be selected and visually inspected in detail. We support a dynamic race position diagram as well as a more detailed lap times line plot for showing the drivers’ lap times in comparison. Many interaction techniques are supported like selections, filtering, highlighting, color coding, or details-on demand. We illustrate the usefulness of our visualization tool by applying it to a Formula one dataset while we describe the different dynamic visual racing patterns for a number of selected races and drivers.
Private equity (PE) firms are investment firms that acquire equity shares in companies. The goal of PE firms is to exit the investment after few years with a substantial increase in value. PE firms often claim to outperform the market, i.e. to create alpha.
The overall aim of this paper is to unravel the mystery of value creation in the PE industry. First, the author presents a conceptual framework for value creation in the PE industry based on a multiple valuation model that breaks down value creation into different elements. Second, the paper evaluates whether PE firms really create value by analysing and combining results from prior empirical studies based on the conceptual framework.
The results show that existing empirical evidence is mixed but that there is indeed a tendency toward a positive evidence that PE firms create economic value in average. However, there are methodological difficulties in measuring the value creation and studies are often subject to bias. Finally, it is pointed out that the question whether PE firms really create value has to be viewed from different perspectives such as the perspective of the PE firm, the investors and the portfolio companies.
Software evolvability is an important quality attribute, yet one difficult to grasp. A certain base level of it is allegedly provided by service- and microservice-based systems, but many software professionals lack systematic understanding of the reasons and preconditions for this. We address this issue via the proxy of architectural modifiability tactics. By qualitatively mapping principles and patterns of Service Oriented Architecture (SOA) and microservices onto tactics and analyzing the results, we cannot only generate insights into service-oriented evolution qualities, but can also provide a modifiability comparison of the two popular service-based architectural styles. The results suggest that both SOA and microservices possess several inherent qualities beneficial for software evolution. While both focus strongly on loose coupling and encapsulation, there are also differences in the way they strive for modifiability (e.g. governance vs. evolutionary design). To leverage the insights of this research, however, it is necessary to find practical ways to incorporate the results as guidance into the software development process.
Due to the consequential impact of technological breakdowns, companies have to be prepared to deal with breakdowns or even better prevent them. In today's information technology, several methods and tools exist to downscale this concern. Therefore, this paper deals with the initial determination of a resilient enterprise architecture supporting predictive maintenance in the information technology domain and furthermore, concerns several mechanisms on how to reactively and proactively secure the state of resiliency on several abstraction levels. The objective of this paper is to give an overview on existing mechanisms for resiliency and to describe the foundation of an optimized approach, combining infrastructure and process mining techniques.
A large body of literature is concerned with models of presence— the sensory illusion of being part of a virtual scene— but there is still no general agreement on how to measure it objectively and reliably. For the presented study, we applied contemporary theory to measure presence in virtual reality. Thirty-seven participants explored an existing commercial game in order to complete a collection task. Two startle events were naturally embedded in the game progression to evoke physical reactions and head tracking data was collected in response to these events. Subjective presence was recorded using a post-study questionnaire and real-time assessments. Our novel implementation of behavioral measures lead to insights which could inform future presence research: We propose a measure in which startle reflexes are evoked through specific events in the virtual environment, and head tracking data is compared to the range and speed of baseline interactions.
Continuous refactoring is necessary to maintain source code quality and to cope with technical debt. Since manual refactoring is inefficient and error prone, various solutions for automated refactoring have been proposed in the past. However, empirical studies have shown that these solutions are not widely accepted by software developers and most refactorings are still performed manually. For example, developers reported that refactoring tools should support functionality for reviewing changes. They also criticized that introducing such tools would require substantial effort for configuration and integration into the current development environment.
In this paper, we present our work towards the Refactoring-Bot, an autonomous bot that integrates into the team like a human developer via the existing version control platform. The bot automatically performs refactorings to resolve code smells and presents the changes to a developer for asynchronous review via pull requests. This way, developers are not interrupted in their workflow and can review the changes at any time with familiar tools. Proposed refactorings can then be integrated into the code base via the push of a button. We elaborate on our vision, discuss design decisions, describe the current state of development, and give an outlook on planned development and research activities.