Theses and Projects

We present all current topics for projects and Bachelor's and Master's theses at the ISF. In addition, we will briefly present our teaching offer for the summer semester 2025. All interested parties are cordially invited.

When? Monday, 27.01.2025, from 16:45 to 18:15

Where? IZ 161 and in the Webex (hybrid)

The slides with the topics will be published here on the same day.

Analyzing the Research Workflow in Python Research Scripts

Context

While every Data Science research script has an input and an output at some point, there are multiple steps in between that are used to transform the provided data. Given a big enough sample, it should be possible to derive a workflow that most scientists adhere to and compare it with suggested workflows described in the literature [1].

This workflow can then be used for further analyses, such as the number of function calls for each stage in the workflow. To facilitate that, we need to create a mapping of popular data science functions to those identified stages.

A short example on how function calls are annotated to a stage in the workflow can be found here:

sample <- read.csv("sample.csv", sep = ";") #import

plot(sample$var1 ~ sample$var2, pch = 20, col = "grey58", ylim = c(0, 1), xlim = c(0, 1)) #visualize

abline(lm(sample$var1 ~ sample$var2)) #visualize

Research Problem

In this work, we want to explore if there is a common workflow across disciplines (such as Chemistry[2], Biology, Social Sciences[3], etc.) that high-grade papers adhere to. By exploring outstanding journals and conferences in that field, we want to collect samples of the way they structure their scripts. The derived workflow is then compared to literature on proposed workflows to check if there is any overlap. Furthermore, we want to create a mapping for the functions used in the process to their respective stage in the derived workflow.

Tasks

• Identify a set of conferences/journals as a basis for a literature review.
• Collect recent publications that do data science in python from this set.
• Derive a multi-stage workflow and compare it to literature on data science workflows.
• Identify popular libraries that are used and map their functions to the stages of your workflow.

Related Work and Further Reading

[1] Huber, F. (2025). Hands-on Introduction to Data Science with Python. v0.23, 2025, Zenodo. https://doi.org/10.5281/zenodo.10074474

[2] Davila-Santiago, E.; Shi, C.; Mahadwar, G.; Medeghini, B.; Insinga, L.; Hutchinson, R.; Good, S.; Jones, G. D. Machine learning applications for chemical fingerprinting and environmental source tracking using non-target chemical data. Environ. Sci. Technol. 2022, 56 (7), 4080–4090. DOI: 10.1021/acs.est.1c06655.

[3] Di Sotto S, Viviani M. Health Misinformation Detection in the Social Web: An Overview and a Data Science Approach. International Journal of Environmental Research and Public Health. 2022; 19(4):2173. https://doi.org/10.3390/ijerph19042173

Contact

Ruben Dunkel

Reengineering of R Research Scripts using gardenR

Context

Data Science in R comes with a certain fallacy. Most of the time, a script is created for a single publication and then left to rot. Additionally, most of the published R scripts are in no state to reproduce results [1]. To combat this single use practice, the gardenR tool has been created. gardenR uses a set of predefined functions calls to create a dependency graph using program slicing, which is then converted into a Software Product Line. While the application has been tested on research data, there has been no evaluation on whether the annotated SPL meets expectations of researchers in the field yet.

Research Problem

In this work, we want to collect publications of recent R Data Science scripts, that are then annotated through gardenR. Those scripts are then hosted on a website of your creation that allows to select configuration of the corresponding Software Product Line, which are created by execution of C Preprocessor functionality on the client-side [2]. The generated variant can then be downloaded. Finally, want to contact the authors of the publication and present our annotated version, including a visual representation of their script. In an interview, the researcher is then questioned on the potential they see in the annotated code and voice further ideas for improvement.

Tasks

• collect recent R Data Science scripts.
• annotate them through gardenR into an SPL.
• create an online configurator that allows for the annotated scripts to be turned into a selected variant using C Preprocessor statements.
• conduct interviews with the researcher that published the script on the usability of the annotated script and its derivatives.

Related Work and Further Reading

[1] Vidoni, Melina. "Software engineering and r programming: A call for research." (2021).

[2] https://gcc.gnu.org/onlinedocs/cpp/Ifdef.html

Contact

Ruben Dunkel

Overview on the Usage of Programming Languages in Data Science

Context

With empirical science creating large sets of data, the discipline of data science is more important than ever to wrangle conclusions from those heaps of unstructured data [1]. There are several popular languages used in Data Science such as Python, R or Julia. While they are generally seen as the most prevalent, there is no data on how popular they are in different disciplines (such as Chemistry, Biology, Social Sciences, etc.).

Research Problem

We want to create a comprehensive overview over the distribution of programming languages and libraries in the different fields of research. By comparing the use and distribution we want to gain insight on what the current stack of tools used for data science looks like.

Tasks

• Create or modify a tool that accesses zenodo and stores data science artifacts by field of research.
• Analyze the artifacts on which language/framework/libraries are used and extract the provided functions of the libraries.
• Create rankings for language/framework/libraries by field of research.

Related Work and Further Reading

[1] Van Der Aalst, Wil. "Process mining: Overview and opportunities." ACM Transactions on Management Information Systems (TMIS) 3.2 (2012): 1–17.

Contact

Ruben Dunkel

Exploring the Usage of Feature Models for Feature Model Analysis Benchmarking

Context

New algorithms and approaches for feature model analysis are typically analyzed empirically for their publication. This process requires feature models that can be used for benchmarking the evaluated algorithm. However, it is unclear which feature models are used in which publication and how impactful the selection is on the results of the evaluation.

Research Problem

Extend the existing feature model benchmark with new feature models and track their usage in existing publications. Identify peculiarities in the generated data set and try to explain found correlations.

Tasks

1. Extend the feature model benchmark for newer publications (2023 till now)
2. Extract which paper uses which feature model in its analysis
3. Analyze the usage behavior of feature models in feature model analyis benchmarking and identify correlations and outliers

Related Work and Further Reading

• Chico Sundermann, Vincenzo Francesco Brancaccio, Elias Kuiter, Sebastian Krieter, Tobias Heß, and Thomas Thüm. 2024. Collecting Feature Models from the Literature: A Comprehensive Dataset for Benchmarking. In Proceedings of the 28th ACM International Systems and Software Product Line Conference (SPLC '24). Association for Computing Machinery, New York, NY, USA, 54–65. https://doi.org/10.1145/3646548.3672590
• github.com/SoftVarE-Group/feature-model-benchmark

Contact

Raphael Dunkel

Analyzing the Replicability of Feature Model Analysis Evaluations

Context

New algorithms and approaches for feature model analysis are typically analyzed empirically for their publication. Reproducing these results is important to validate research findings, ensure scientific integrity, and allow for the re-usage of tools in further research. However, often the evaluation is not easily reproducible because of missing data or broken tooling.

Research Problem

Replicate existing research in the context of feature model analysis by generating functioning replication packages and partially re-computing their evaluations. Further try to reproduce these findings on new and unused feature models.

Tasks

1. Select relevant studies that evaluated the performance of a feature model analysis algorithm (criteria may be provided)

2. Replicate the selected studies

3. Reproduce the selected studies on a small subset of new feature models

Related Work and Further Reading

• Chico Sundermann, Vincenzo Francesco Brancaccio, Elias Kuiter, Sebastian Krieter, Tobias Heß, and Thomas Thüm. 2024. Collecting Feature Models from the Literature: A Comprehensive Dataset for Benchmarking. In Proceedings of the 28th ACM International Systems and Software Product Line Conference (SPLC '24). Association for Computing Machinery, New York, NY, USA, 54–65. https://doi.org/10.1145/3646548.3672590

• Carver, J.C., Juristo, N., Baldassarre, M.T. et al. 2014. Replications of software engineering experiments. Empir Software Eng 19, 267–276. doi.org/10.1007/s10664-013-9290-8

Contact

Raphael Dunkel

Exploring Effort-aware Feature Selection for Machine Learning on Feature Models

Context

Feature selection is an important step in modern machine learning pipelines. It helps to keep machine learning models simple, helps with interpretability, and can even improve model performance in some cases. However, difficult to compute feature sets require high time investments and can have a high negative climate impact. Effort-aware feature selection tries to address this issues by not only optimizing the performance of the trained ML models, but also the computative expensiveness of the selected features.

Research Problem

Select and evaluate different effort-aware feature selection strategies in the context of machine learning on feature models.

Tasks

1. Systematically select effort-aware feature selection techniques that represent different approaches to this problem
2. Integrate/Implement the selected approaches into _fe4femo_, a feature engineering framework for feature models
3. Evaluate the effectiveness and trade-offs of the implemented techniques on the feature model benchmark

Related Work and Further Reading

• Isabelle Guyon, Steve Gunn, Masoud Nikravesh, and Lotfi A. Zadeh. 2006. Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing). Springer-Verlag, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-35488-8

• Hamdani, T.M., Won, JM., Alimi, A.M., Karray, F. 2007. Multi-objective Feature Selection with NSGA II. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_27  

• Yong Zhang, Dun-wei Gong, and Jian Cheng. 2017. Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification. IEEE/ACM Trans. Comput. Biol. Bioinformatics 14, 1 (January 2017), 64–75. https://doi.org/10.1109/TCBB.2015.2476796 

Contact

Raphael Dunkel

Exploring the Impact of Feature Transformations for Machine Learning on Feature Models

Context

Feature transformation is an important step in modern machine learning pipelines. It can improve model performance by for example removing collinearity or make model interpretation easier, e.g., through dimensionality reduction of the feature space. However, the impact and trade-off between various feature transformation techniques has not yet been evaluated in the context of machine learning on feature models.

Research Problem

Select and evaluate different feature transformation strategies in the context of machine learning on feature models.

Tasks

1. Systematically select feature transformation techniques that represent different approaches to this problem

2. Integrate/Implement the selected approaches into _fe4femo_, a feature engineering framework for feature models

3. Evaluate the effectiveness of the implemented techniques on the feature model benchmark

Related Work and Further Reading

• Kuhn, M., Johnson, K. 2013. Data Pre-processing. In: Applied Predictive Modeling. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6849-3_3

• Liu, Huan, and Hiroshi Motoda. 1998. Feature transformation and subset selection. In IEEE Intell Syst Their Appl 13.2, 26-28. https://doi.org/10.1109/MIS.1998.671088

• Wang, Yasi and Yao, Hongxun and Zhao, Sicheng. 2015. Auto-Encoder Based Dimensionality Reduction. Neurocomputing. 184. https://doi.org/10.1016/j.neucom.2015.08.104 

Contact

Raphael Dunkel