Humboldt-Universität zu Berlin - Faculty of Mathematics and Natural Sciences - Process Management and Information Systems

Bachelor and Master Thesis

Our team continuously offers bachelor and master thesis topics to be written in English. For the current topic list, see below. Furthermore, find here a summary of guidelines for working on your thesis with us.



Please consider the following hints and guidelines for working on your thesis:

  • The thesis has to be written using the \documentclass[preprint,review,12pt]{elsarticle} Latex template. You can use the overleaf template for writing your thesis.
  • A bachelor thesis has a page limit of 40 pages of text (not including cover, table of content, references, appendices).

  • A master thesis has a page limit of 80 pages of text (not including cover, table of content, references, appendices).



The candidate is expected to be familiar with the general rules of writing a scientific paper. Some general references are helpful for framing any thesis, no matter which topic:

In agreement with the supervisor an individual list of expected readings should be studied by the student in preparation of the actual work on the thesis.



The grading of the thesis takes various criteria into account, relating both to the thesis as a product and the process of establishing its content. These include, but are not limited to:

  • Correctness of spelling and grammar

  • Aesthetic appeal of documents and figures

  • Compliance with formal rules

  • Appropriateness of thesis structure

  • Coverage of relevant literature

  • Appropriateness of research question and method

  • Diligence of own research work

  • Significance of research results

  • Punctuality of work progress

  • Proactiveness of handling research progress


Recent Topics

If you are interested in one of the following topics, please send an email expressing your interest to Prof. Dr. Jan Mendling. Please explain why this topic is interesting for you and how it fits your prior studies. Also explain what are your strengths in your studies and in which semester of your studies you are.

The next deadline is 15 June 2022 .


Topic 1: Review of Process Models before the end of World War I

Process modeling has its roots in the scientific management of the late 19th and the early 20th century. So far, research publications around that time has never been analyzed for process models and related visualizations. The goal of this thesis is to conduct a systematic review of the literature, identify different categories of models and visualizations, and compare them with contemporary concepts.



Topic 2: Visual Analytics of Waiting Times for Process Mining

Process mining is a family of analysis techniques that takes event sequence data as input and generates meaningful visual representations for analysts. So far, the analysis of waiting times has been limited in prior research. The goal of this thesis is to review contributions that explicitly show the timeline of event, identify opportunities for improvement and implement and evaluate a new visualization technique.



Topic 3: Process Science on Causal Event Graphs – the Logical Next Step

Nowadays, most process mining tools are concentrated on discovering process models and process performance indicators from event-log data by the means of common algorithms, mostly originated from the alpha algorithm. This helps analyst to gain insights into e.g., which paths the process takes, how many variants it has, how complex it is, or where the bottlenecks are located. Nevertheless, most of the time, those algorithms neglect the contextual data that is connected to its execution but has no obvious impact on it, even though it could be valuable for the root-cause detection of issues.
The goal of this thesis is to investigate a special kind of data structure which Waibel et al. call “Causal Event Graph” (stored in a Neo4j graph database) with the help of Jupyter Notebooks (will be provided on the Azure Cloud). The challenge of this thesis is to develop Python scripts that make use of common AI/ML libraries with the goal to detect patterns or cluster data in order to gain insights that go beyond “classical” process mining.


  • Vom Brocke, J., van der Aalst, W., Grisold, T., Kremser, W., Mendling, J., Pentland, B., Recker, J., Roeglinger, M., Rosemann, M. and Weber, B., 2021. Process Science: The Interdisciplinary Study of Continuous Change. Available at SSRN 3916817.
  • Waibel, P., Pfahlsberger, L., Revoredo, K. and Mendling, J., 2022. Causal Process Mining from Relational Databases with Domain Knowledge. arXiv preprint arXiv:2202.08314.
  • Van der Aalst, W.M., 2016. Process mining: data science in action. Springer.


Topic 4: Use of Business Process Models in Requirements Engineering

Business Process Models have been widely used in software development for requirement engineering activities. However, there is no study that presents an overview of how these models are used depending on the software development approach in which they are applied. The goal of this thesis is to conduct a systematic review of the literature, identify and compare how business process models are used for requirements engineering activities depending on the software development methodology applied (eg, agile or traditional approaches), as well as prioritize these uses depending on the methodology.

  • Wagner, S., Fernández, DM, Felderer, M., Vetrò, A., Kalinowski, M., Wieringa, R., ... & Winkler, D. (2019). Status quo in requirements engineering: A theory and a global family of surveys. ACM Transactions on Software Engineering and Methodology (TOSEM), 28(2), 1-48.
  • Cardoso, ECS, Almeida, JPA, & Guizzardi, G. (2009, September). Requirements engineering based on business process models: A case study. In 2009 13th Enterprise Distributed Object Computing Conference Workshops (pp. 320-327). IEEE.
  • De la Vara González, JL, & Diaz, JS (2007, June). Business process-driven requirements engineering: a goal-based approach. In Proceedings of the 8th workshop on business process modeling (p. 15).


Topic 5: Uses of Models in Agile Software Development

Modeling is a key topic in software engineering. In software development projects, among other aspects, modeling supports the developer in understanding the design by providing an overview and a tool for communication with fellow developers and other stakeholders. The benefits of models for supporting system analysis and design activities have been highlighted regarding their cognitive effectiveness, often regarding traditional methodologies. However, these benefits have also been discussed in the agile scene, but it is still not clear to what extent models are used in agile software development projects. The thesis aims to conduct a systematic review of the literature to identify the status quo on the topic. The findings shall be evaluated according to the perspective of practitioners.

  • Ambler, Scott W. The object primer: Agile model-driven development with UML 2.0. Cambridge University Press, 2004.
  • Alfraihi, Hessa Abdulrahman A., and Kevin Charles Lano. "The integration of agile development and model driven development: A systematic literature review." The 5th International Confrence on Model-Driven Engineeing and Software Development (2017).
  • Wagner, Stefan, Daniel Méndez Fernández, Michael Felderer, Antonio Vetrò, Marcos Kalinowski, Roel Wieringa, Dietmar Pfahl et al. "Status quo in requirements engineering: A theory and a global family of surveys." ACM Transactions on Software Engineering and Methodology (TOSEM) 28, no. 2 (2019): 1-48.
  • Petre, Marian. "UML in practice." In 2013 35th international conference on software engineering (icse), pp. 722-731. IEEE, 2013.


Topic 6: A Study into the Practice of Logging in the Software Development Process

Logging is a crucial practice in software development as it facilitates the monitoring and resolution of problems that arise on runtime. While there is much research focusing on log analysis, the understanding of logging practices is still underdeveloped. Typical works have focused on improving logging by suggesting where and what to log. However, there are no clear guidelines on what the benefit are of logging at specific point in program and what is their contribution to the overall process improvement. The goal of this thesis is to review existing literature on logging practices and extract requirements for analyzing such logs from a process point of view.

  • H Li, W Shang, B Adams, M Sayagh, and AE Hassan, "A Qualitative Study of the Benefits and Costs of Logging From Developers' Perspectives," in IEEE Transactions on Software Engineering, vol. 47, no. 12, pp. 2858-2873, 1 Dec. 2021, doi: 10.1109/TSE.2020.2970422.
  • He, Pinjia, et al. "Characterizing the natural language descriptions in software logging statements." Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering. 2018
  • Yuan, Ding, Soyeon Park, and Yuanyuan Zhou. "Characterizing logging practices in open-source software." 2012 34th International Conference on Software Engineering (ICSE). IEEE, 2012.


Topic 7: Analyzing File Evolution Trends for Predicting Development Task Completion

Software development is supported by central repositories, such Version Control Systems or Issue Tracking Systems. These repositories keep track of the evolution of the artifacts being created during the development process.

The evolution of artifacts in real world software projects can give important insights on the effort put on determined development tasks. Understanding trends in the software evolution can help to better predict the time and the effort taken by specific artifacts to evolve towards a final version.

This work should investigate how we can use time-series analysis to understand and predict the status of software development tasks. Real world data from open-source repositories can be found online.

  • Saimir Bala, Kate Revoredo, João Carlos de A. R. Gonçalves, Fernanda Baião, Jan Mendling, Flávia Maria Santoro:
    Uncovering the Hidden Co-evolution in the Work History of Software Projects. BPM 2017: 164-180
  • Ruohonen, Jukka, Sami Hyrynsalmi, and Ville Leppänen. "Time series trends in software evolution." Journal of Software: Evolution and Process 27.12 (2015): 990-1015.


Topic 8: Business Processes and Benchmarking

Benchmarking plays a very important role for many businesses. A key focus of benchmarking are business processes that are fairly standardized in different industries. One of the key challenges of benchmarking is to obtain benchmarking data for business processes that are not fully publicly visible. The objective of this thesis is to establish a theoretical perspective on how benchmarking can be applied, which data sources are available for business processes and corresponding performance indicators, and how innovative concepts for addressing the challenges of benchmarking can be developed.

  • Aksu, Ü., & Reijers, HA (2020, October). How Business Process Benchmarks Enable Organizations To Improve Performance. In 2020 IEEE 24th International Enterprise Distributed Object Computing Conference (EDOC) (pp. 197-208). IEEE.
  • Blackburn, JD (1992). Time-based competition: white-collar activities. Business Horizons , 35 (4), 96-102.
  • De Toni A & Meneghetti A (2000). Traditional and innovative paths towards time-based competition. International Journal of Production Economics , 66 (3), 255-268.