Sebastian HönelDoctoral student
Department of Computer Science and Media Technology Faculty of Technology
- You will find me in building D, room D2236H.
- I have a public calendar where you will find my office hours at bit.ly/2XY1RZ1
My research groups
Data Intensive Software Technologies and Applications (DISTA) The research group Data Intensive Software Technologies and Applications studies data-driven approaches, such as machine learning,…
Data-driven Software and Information Quality Within the research area Data-driven Software and Information Quality, the objective of Linnaeus University Centre for Data Intensive Sciences and…
My ongoing research projects
Article in journal (Refereed)
Hönel, S., Ericsson, M., Löwe, W., Wingkvist, A. (2020). Using source code density to improve the accuracy of automatic commit classification into maintenance activities. Journal of Systems and Software. 168. 1-19.
Conference paper (Refereed)
- Hönel, S., Ericsson, M., Löwe, W., Wingkvist, A. (2019). Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security (QRS). 109-120.
- Ulan, M., Hönel, S., Martins, R.M., Ericsson, M., Löwe, W., et al. (2018). Quality Models Inside Out : Interactive Visualization of Software Metrics by Means of Joint Probabilities. Proceedings of the 2018 Sixth IEEE Working Conference on Software Visualization, (VISSOFT), Madrid, Spain, 2018. 65-75.
- Hönel, S., Ericsson, M., Löwe, W., Wingkvist, A. (2018). A changeset-based approach to assess source code density and developer efficacy. ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings. 220-221.
Hönel, S., Pícha, P., Brada, P., Rychtarova, L. (2021). Detection of the Fire Drill anti-pattern: Nine real-world projects with ground truth, issue-tracking data, source code density, models and code.
A dataset comprised of various files, such as CSV or Excel spreadsheets, notebooks, and code in R, pre-computed results as RDS, etc..
Hönel, S. (2019). 359,569 commits with source code density; 1149 commits of which have software maintenance activity labels (adaptive, corrective, perfective).
A dataset as an SQL-importable file, compatible with MariaDB and MySQL..
Hönel, S. (2020). mmb : Arbitrary Dependency Mixed Multivariate Bayesian Models.
mmb is a package for the R statistical software environment..
Ulan, M., Hönel, S., Martins, R.M., Ericsson, M., Löwe, W., et al. (2018). Artifact: Quality Models Inside Out : Interactive Visualization of Software Metrics by Means of Joint Probabilities.
The artifact is a VirtualBox virtual machine (VM). Part of this bundle is a file with instructions. Please read those first..
Conference paper (Other academic)
- Hönel, S., Ericsson, M., Löwe, W., Wingkvist, A. (2019). Bayesian Regression on segmented data using Kernel Density Estimation. 5th annual Big Data Conference : Linnaeus University, Växjö, Sweden, 5-6 December 2019.
Report (Other academic)
Other (Other academic)
Hönel, S. (2020). Git Density : Analyze git repositories to extract the Source Code Density and other Commit Properties.
Git Density is a software suite to analyze git-repositories with the goal of detecting the source code density and other properties of the software, such as metrics..
Licentiate thesis, comprehensive summary (Other academic)
- Hönel, S. (2020). Efficient Automatic Change Detection in Software Maintenance and Evolutionary Processes. Licentiate Thesis. Växjö, Faculty of Technology, Linnaeus University. 37.