Abschlussarbeitsthemen
Vielen Dank für Ihr Interesse, eine Bachelor- oder Masterarbeit am Lehrstuhl für Wirtschafts- informatik insb. Informationssystemmanagement zu schreiben. Nachfolgend finden Sie die aktuelle Liste unserer Forschungsthemen. Sofern nicht anders angegeben, sind die Themen sowohl für Bachelor- als auch Masterstudierende und die Bearbeitung kann sofort begonnen werden. Die bevorzugte Sprache ist Englisch.
Wenn Sie sich für eines der Themen interessieren, nutzen Sie bitte das Anmeldeformular am Ende der Website. Bewerben Sie sich idealerweise 8 Wochen vor Ihrem geplanten Start.
Wichtig für Bachelor-Studierende: Erfahrung im wissenschaftlichen Arbeiten im Bereich IS/MIS wird vorausgesetzt, d.h. erfolgreicher Abschluss des Moduls WAWI und/oder erfolgreiche Teilnahme an einem ISM-Seminar.
Bedingungen für eine Abschlussarbeit am ISM-Lehrstuhl: Bei einer Anfertigung einer Abschlussarbeit am Lehrstuhl für Wirtschaftsinformatik insb. Informationssystemmanagement verpflichten sich die Studierenden zu einer regelm??igen Teilnahme am Kolloquium für Abschlussarbeiten. W?hrend dieses Kolloquiums sind zwei Vortr?ge (Zwischenvortrag + Abschlussvortrag/Verteidigung) zu halten. Das Kolloquium findet ungef?hr alle drei Wochen am Dienstagabend statt.
Themen
Entwicklung eines Dashboards für das Monitoring von Studieng?ngen der Fakult?t WIAI
Das Qualit?tsmanagement der Fakult?t WIAI arbeitet derzeit an der Entwicklung eines Kennzahlensystems zur Beobachtung der Qualit?t von Studium und Lehre. Aufgabe dieser Abschlussarbeit (Bachelor- oder Master) ist es, zur Konzeption des System beizutragen und auf Basis der zur Verfügung stehenden Rohdatensysteme ein Aggregationssystem (Excel oder Tableau) und darauf aufbauend ein Dashboard für die Fakult?tsleitung, das Studiendekanat und die Studiengangbeauftragten zu implementieren.
Methode: Design Science, Entwicklungsarbeit
Sprache: Vorzugsweise deutsch
Betreuer: Prof. Dr. Daniel Beimborn, Rosemarie Fleck
Beginn: so bald wie m?glich
Revisiting Conway's Law: Examining the Impact of Organizational Structure on Software Architecture and its Development Process
Conway's Law asserts that the architecture of software systems mirrors the communication structures of the organizations that design them (Conway, 1968). For companies aiming to achieve modular, scalable, and efficient software architectures, aligning their organizational structures and processes with architectural goals is both a challenge and an opportunity.
This master’s thesis investigates how organizational factors, such as team structures, collaboration patterns, and communication flows, influence the modularity and evolution of software architecture. The study will focus on real-world organizational settings, studying the development process of modular software systems to identify best practices and pitfalls.
What practices enhance or hinder the modularity of software systems? How does evolving software modularity feedback affect organizational processes? Can Conway’s hypothesis be observed?
Methodology:
The thesis shall employ a qualitative case analysis approach.
Language of the thesis: English or German (preferably English)
Getting started with literature:
- Conway, M. E. (1968). How do committees invent?. Datamation, 14(4), 28–31.
- Haki, K., & Legner, C. (2021). The mechanics of enterprise architecture principles. Journal of the Association for Information Systems, 22(5), p. 1334–1375.
- MacCormack, A., Baldwin, C., & Rusnak, J. (2012). Exploring the duality between product and organizational architectures: A test of the “mirroring” hypothesis. Research Policy, 41(8), 1309–1324.
Supervisor: Elias Grewe
Assessing the Role of GenAI generated Code in Due Diligence - Implications for Mergers & Acquisitions
Tools that leverage GenAI technology have been shown to enhance user productivity in content creation tasks. Noy and Zhang (2023) found that writers using OpenAI’s ChatGPT produced content more quickly while maintaining improved overall quality. Similarly, GitHub's Copilot, marketed by Microsoft, claims to increase software development speed by 55.8% (Peng et al., 2023).
GitHub Copilot is powered by Codex, a large language model trained on publicly available GitHub repositories (Chen et al., 2021). It assists software developers in two primary ways: through a conversational interface, similar to ChatGPT, which answers software engineering queries, and through advanced code completion mechanisms that streamline coding tasks and also generate complete parts of software code.
While tools like GitHub Copilot, ChatGPT, and other GenAI models are being rapidly adopted by software developers worldwide, their impact on due diligence (DD) and mergers and acquisitions (M&A) remains unclear. Solutions such as Sema (Sema Software) enable organizations to scan codebases for AI-generated code to assess potential risks. However, the specific risks and benefits of GenAI-generated code in the context of DD and M&A, particularly its influence on firm valuation, are not yet well understood.
This master's thesis examines the impact of AI-generated code on the M&A process, specifically its implications during due diligence. It aims to evaluate the potential risks, opportunities, and overall effects of AI-generated code in acquisitions. The key questions explored include: How can AI-generated code positively influence due diligence? And, in what cases might its use pose risks that could jeopardize an acquisition?
Methodology:
Case-study
Language of the thesis: English
Getting started with literature:
- Andriole, S. (2007). Mining for Digital Gold: Technology Due Diligence for CIOs.
Communications of the Association for Information Systems, 20, pp-pp.
https://doi.org/10.17705/1CAIS.02024 - Noy, S., Zhang, W., 2023. Experimental evidence on the productivity effects of
generative artificial intelligence. Science 381, 187–192.
https://doi.org/10.1126/science.adh2586 - Peng, S., Kalliamvakou, E., Cihon, P., Demirer, M., 2023. The Impact of AI on Developer
Productivity: Evidence from GitHub Copilot. - Chen, M., Tworek, J., Jun, H., ..., 2021. Evaluating Large Language Models Trained on
Code. https://doi.org/10.48550/arXiv.2107.03374 - Bain & Company, 2024. Harnessing Generative AI in Private Equity,
www.bain.com/insights/harnessing-generative-ai-global-private-equity-report-
2024/
Supervisor: David Rochholz