Mobile apps are nowadays the preferred means to accomplish ubiquitous tasks like messaging, e-commerce and even playing games. Often, there exist multiple apps for the same purpose, and it is the choice of the end user to pick an appropriate app. Apps that behave unexpected, e.g., crash frequently, are sooner or later replaced, which isundesirable for the companies developing such apps. Thus, it is essential to tests apps properly before they are released onto the market. However, testing manually is often not only too cost-intensive but also too time-consuming in the short development phase, thus an automated solution is preferred. Testing mobile apps automatically received increased attention in the last decade from primarily people in academia, and several testing techniques evolved. One technique that yielded promising results, especially in different domains, is search-based software testing in which a metaheuristic, e.g., a genetic algorithm, is applied to solve an optimisation problem, e.g., test generation. A main objective of test generation is to produce tests that reveal as many faults as possible. This in turn requires the generation of tests that deeply explore the tested app. The core metric to quantify how much code tests cover is the measurement of code coverage, which can be computed at different levels of granularity ranging from determining the fraction of covered activities to a very fine-grained measurement that calculates the percentage of covered lines. This coverage information is then often used to guide the search of the employed metaheuristic. However, current automated test generation approaches produce tests with a rather low code coverage. Thus, a substantial part of tested apps remains unexplored, which in turn misses revealing deeply residing faults. We identified three core issues that are directly related to the generation of low-coverage tests. First, the applicability of current test generators is often limited. This comprises the fact that current state-of-the-art code coverage tools are incapable of instrumenting a substantial number of apps and consequently, test generators cannot utilise detailed coverage information during exploration. In addition, test generators are often only equipped with a primitive set of actions that are insufficient to simulate system events and complex user inputs. Second, the test execution is extremely time-consuming. This includes among other things the overhead associated with executing individual actions, intermediate restart operations as well as fitness evaluations. Since search-based algorithms require a substantial number of test executions to play out their strengths, the slow test execution impedes the effectiveness of the search. Third, the guidance offered by search-based algorithms is often hampered by applying inadequate fitness functions or by using non-representation-specific variation operators. In this thesis we address the problem of low-coverage tests in the Android domain by proposing several enhancements for the three identified core issues. Concerning the applicability problem, we provide the implementation of a robust code coverage tool that is capable of measuring coverage at different levels of granularity and requires no access to the source code. We also propose to include actions that can simulate system events as well as complex user inputs. Regarding the performance issue, we suggest the integration of a surrogate model that is capable of predicting the outcome of individual actions or complete tests over time in order to reduce the overall test execution costs. With respect to the lack of guidance offered by traditional search-based algorithms, we suggest alternative search strategies. In the case of a deceptive fitness landscape, we propose using novelty search algorithms. Alternatively, we suggest utilising estimation of distribution algorithms that require no crossover or mutation perators to sample new tests. While all those enhancements had a positive impact on the Android test generation process, the individual empirical studies highlighted that further research is necessary to unleash the full power of the proposed search-based algorithms. In particular, exploring complex user interfaces meaningfully requires more attention whether by introducing additional actions or by extracting valuable hints to infer reasonable text inputs. In addition, the guidance offered by fitness functions is often limited because they are either designed too coarse at all or do not accurately reflect the search objectives.

With recent progresses in the field of artificial intelligence (AI), algorithms can support more complex tasks (Frey and Osborne, 2017) that seemed safe from automation a few years ago (Brynjolfsson and McAfee, 2012). They can even outperform human decision-making in many task domains (Meehl, 1954; Dawes et al., 1989). The prominent defeat of Garry Kasparov, the world champion in chess, in 1997 is a milestone for the potentials of algorithms, increasing the value of IBM’s stock increased by $18 billion (Norvig and Russell, 2010). More recently, universal chatbots such as ChatGPT and the Bing Chatbot Sydney can influence millions of users and provide them answers to a broad range of tasks. To gain a competitive edge, companies invest heavily in algorithmic systems (Kappelman et al., 2021), which are considered as one of the most important drivers of today’s economy (Brynjolfsson and McAfee, 2011; Hogharth and Makridakis, 1981; Önkal et al., 2019). Particularly in management accounting, an algorithmic data analysis offers the potential to support a high decision quality, making their usage a key factor for success. However, algorithms can not only support, but also manipulate human decision-making (Roose et al., 2023). Algorithmic advice can lead to overtrust, i.e. to the heuristic replacement of vigilant information seeking and processing in favor of the advice provided (Mosier et al., 2001). The implementation of algorithmic decision support systems is thus no panacea to improve human decision-making. In contrast, they can entail new risks, such as the uncritical use of these algorithms (Boden, 2016). Yet, this ‘dark side’ of algorithms is not sufficiently investigated in the field of management accounting. With advances in machine learning, algorithmic support can become even more of a black box, blurring the reference points for evaluating its advice quality and thus increasing the risk of overtrust. Stephen Hawking even warns that AI will be “either the best, or the worst thing, ever to happen to humanity” (Alex Hern in: The Guardian: Stephen Hawking: AI will be 'either best or worst thing' for humanity, 19th October 2016). Interacting with such algorithmic systems therefore requires data literacy skills for an appropriate use. They seem to be a crucial prerequisite in today’s data-driven world to reap of the benefits of algorithmic systems. If no reflective and critical interaction between human and algorithm can be ensured, the trend towards a digitalized world will be a race against the algorithm (Brynjolfsson and McAfee, 2011). This dissertation takes this understudied critical perspective on the increasing implementation of algorithmic systems and sheds light on different aspects of human-algorithm interaction, ranging from the ability to use data visualization tools to trust calibrations and critical evaluations of algorithmic advices. Study I investigates an adequate information representation of subjective multiple criteria decision problems, an important task in management accounting, to support human decision-making. Study II distinguishes different designs of the human-algorithm interaction investigated in the trust in automation literature and compiles factors influencing the calibration of trust and behavior toward the true capabilities of the algorithm. Study III examines the role of performance feedback on trust and advice usage in a forecasting task for a better understanding of the recently observed phenomenon ‘algorithm aversion’. Finally, Study IV focusses on the risk of overtrust in advice in repeated interactions with a forecasting advisor and investigates the calibration of advice usage. Each study thereby represents an independent contribution and contains all information relevant to the respective research questions asked in the study.

Coastal flooding events pose an existential threat to populations around the world, particularly in the Global South. As a result of climate change and other, direct forms of anthropogenic interference, the magnitude and frequency of such floodings is predicted to continuously increase in the near future. Therefore, it is important to understand the vulnerability of affected people to implement appropriate measures. This research presents a case study on coastal flooding and its impact on livelihoods in the Volta Region of Ghana. The paper utilizes a vulnerability framework that focuses on exposure, susceptibility, as well as the capacities to anticipate, cope with, and recover from the effects of natural hazards. Based on a qualitative approach, the study focusses on those households who were directly affected of a severe tidal flood in November 2021, leading to a total destruction of their property. The research was conducted in March, July and August 2022. It revealed that the studied group had low capacity to anticipate and had no contingency plans in case of relocation. Further, we discovered different strategies that people applied to deal with the situation eight months after the flood event. Most households applied (short term) coping strategies to reestablish their former marine-based livelihood systems. Adaption in terms of long-term adjustment of their livelihoods played only a minor role and could be found in form of out-migration in a limited number of households.

This study explores American folk horror as a mode of inquiry into the literary, cultural, filmic, ecological, and political histories of the United States. By using ecofeminist theory to read American folk horror against its roots in the frontier gothic, a genre concerned with the realities and afterlives of American settler colonialism, in turn inextricably linked with the U.S.' founding myths and national narratives that developed since the arrival of the first European settlers, my approach uncovers the dual and simultaneous exploitation of women and nature since 1492, focusing on the ways human and non-human Others continue to be at the mercy of anthropocentric and capitalocentric systems. American folk horror as genre, and the frontier gothic as its archive, allows us to see the ways American founding narratives serve as and word like folklore, contributing to the perpetuation of discrimination against underprivileged groups, especially those who are seen as nature, like women. This book offers a lengthy theorization of the genre as well as both ecofeminist readings of select texts of American folk horror (Darren Aronofsky's mother!, Shirley Jackson's We Have Always Lived in the Castle, Robert Eggers's The VVitch: A New-England Folktale) and a first discussion of a potential corpus of the genre ranging from frontier gothic literature and contemporary film to video games of the 2020s.

In ständiger Rechtsprechung sanktioniert das Bundesarbeitsgericht fehlerhafte Massenentlassungsverfahren mit der Unwirksamkeit der Entlassungen, § 134 BGB i. V. mit § 17 KSchG. Die Arbeit untersucht anhand einer dreistufigen Prüfung, ob und wie geschehene Fehler zu sanktionieren sind. 1) Fehler sind immer dann zu sanktionieren, wenn sie den Sinn und Zweck des Verfahrens tangieren, wenn sie also relevant sind. 2) Arbeitnehmer:innen können sich auf einen Fehler nur berufen, wenn sie von diesem betroffen sind. 3) Erscheint eine Sanktion im konkreten Einzelfall als unangemessen, ist eine Ausnahme vom Unwirksamkeitsverdikt zu gewähren – andere, ggf. weniger weitreichende Rechtsfolgen kommen dann aber ebenfalls nicht in Betracht.