Sebastian Hönel
Doctoral 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
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Data Intensive Software Technologies and Applications (DISTA) The research group Data Intensive Software Technologies and Applications studies data-driven approaches, such as machine learning,…
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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
Publications
Article in journal (Refereed)
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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.
Status: Published
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.
Dataset (Refereed)
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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.
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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.
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Other (Refereed)
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Hönel, S. (2020). mmb : Arbitrary Dependency Mixed Multivariate Bayesian Models.
mmb is a package for the R statistical software environment.
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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.
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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)
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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.
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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.