The DOI matches its Crossref record; 37 of the 38 references it cites were found in a registry or an index, and 1 are not checked yet.
Pitfalls and guidelines for using time-based Git data
Open accessAbstract
Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could affect the research results. This paper presents an extended survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of the 754 technical track and data papers published in MSR 2004–2021, we saw at least 290 (38%) papers utilized time-based data. We also observed that most time-based data used in research papers comes in the form of Git commits, often from GitHub. Based on those results, we then used the Boa and Software Heritage infrastructures to help identify and quantify several sources of dirty Git timestamp data. Finally we provide guidelines/best practices for researchers utilizing time-based data from Git repositories.
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Cites
The promises and perils of mining GitHub
Kalliamvakou, E., Gousios, G., Blincoe, K., Singer, L., German, D. M., & Damian, D. (2014) · Proceedings of the 11th Working Conference on Mining Software Repositories · doi:10.1145/2597073.2597074 · Cited by 770
An extensive comparison of bug prediction approaches
D'Ambros, M., Lanza, M., & Robbes, R. (2010) · 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010) · doi:10.1109/msr.2010.5463279 · Cited by 637
The promises and perils of mining git
Bird, C., Rigby, P. C., Barr, E. T., Hamilton, D. J., German, D. M., & Devanbu, P. (2009) · 2009 6th IEEE International Working Conference on Mining Software Repositories · doi:10.1109/msr.2009.5069475 · Cited by 311
An in-depth study of the promises and perils of mining GitHub
Kalliamvakou, E., Gousios, G., Blincoe, K., Singer, L., German, D. M., & Damian, D. (2016) · Empirical Software Engineering · doi:10.1007/s10664-015-9393-5 · Cited by 282
A Large-Scale Study About Quality and Reproducibility of Jupyter Notebooks
Pimentel, J. F., Murta, L., Braganholo, V. P., & Freire, J. (2019) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2019.00077 · Cited by 264
Boa: A Language and Infrastructure for Analyzing Ultra-Large-Scale Software Repositories
Dyer, R., Nguyen, H. A., Rajan, H., & Nguyen, T. N. (2013) · Cited by 246
Self-citationPreprocessing CVS data for fine-grained analysis
Zimmermann, T. (2004) · International Conference on Mining Software Repositories (MSR) · doi:10.1049/ic:20040466 · Cited by 215
An Empirical Analysis of the Docker Container Ecosystem on GitHub
Cito, J., Schermann, G., Wittern, E., Leitner, P., Zumberi, S., & Gall, H. C. (2017) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2017.67 · Cited by 137
Mining duplicate questions in stack overflow
Ahasanuzzaman, M., Asaduzzaman, M., Roy, C. K., & Schneider, K. A. (2016) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/2901739.2901770 · Cited by 127
Karampatsis, R.-M., Sutton, C., Informatics, S. O., Institute for Machine Learning, & Intelligence, D. S. A. A. (2020) · Edinburgh Research Explorer (University of Edinburgh) · doi:10.5281/zenodo.3653444 · Cited by 117
Research works printed but not linked: 1
- Cosmo RD, Zacchiroli S (2017) Software Heritage: why and how to preserve software source code. In: iPRES 2017: 14th international conference on digital preservation. Kyoto, Japan
Cited by
On the suitability of hugging face hub for empirical studies
Ait, A., Izquierdo, J. L., & Cabot, J. (2025) · Empirical Software Engineering · doi:10.1007/s10664-024-10608-8 · Cited by 21
Replicability Study: Corpora For Understanding Simulink Models & Projects
Shrestha, S. L., Chowdhury, S. A., & Csallner, C. (2023) · Proceedings of the ... ACM-IEEE International Symposium on Empirical Software Engineering and Measurement/International Symposium on Empirical Software Engineering and Measurement · doi:10.1109/esem56168.2023.10304867 · Cited by 10
EvoSL: A Large Open-Source Corpus of Changes in Simulink Models & Projects
Shrestha, S. L., Boll, A., Chowdhury, S. A., Kehrer, T., & Csallner, C. (2023) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS) · doi:10.1109/models58315.2023.00024 · Cited by 8
OpenLambdaVerse: A Dataset and Analysis of Open-Source Serverless Applications
Moreno, Á. C. C., & Abad, C. L. (2025) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e65552.2025.00032 · Cited by 5
Altered Histories in Version Control System Repositories: Evidence from the Trenches
Rapaport, S., Pautet, L., Tardieu, S., & Zacchiroli, S. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00181 · Cited by 3
Metadata
- Authors
- Flint, S. W., Chauhan, J., & Dyer, R.
- Affiliations
- University of Nebraska–Lincoln (US)
- Corresponding author
- Flint, Samuel W.
- Venue
- Empirical Software Engineering
- Published
- 2022-10-06
- Volume
- 27
- Issue
- 7
- ISSN
- 1382-3256 (ISSN-L)
- 1573-7616
- OpenAlex
- W4302293071
- Cited by
- 9
- References
- 37
- Citations by year
- 2026: 1 · 2025: 6 · 2023: 2
- FWCI
- 1.71
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
- Locations
- Empirical Software Engineering (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Lincoln (University of Nebraska) (submittedVersion)
- Lincoln (University of Nebraska) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Computer Science Applications › Open Source Software Innovations
- Keywords
- dirty data, data provenance, GitHub, Software Heritage, Boas, software repositories
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