# Escaping the Time Pit: Pitfalls and Guidelines for Using Time-Based Git Data

arXiv: https://arxiv.org/abs/2103.11339 · HTML: https://mlsanity.com/paper/2103.11339/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [An Empirical Study on Data Leakage and Generalizability of Link Prediction Models for Issues and Commits](https://mlsanity.com/paper/2211.00381/) (2022)

## Abstract (arXiv)

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 the first survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of the 690 technical track and data papers published in MSR 2004--2020, we saw at least 35% of papers utilized time-based data. We then used the Boa and Software Heritage infrastructures to help identify and quantify several sources of dirty commit timestamp data. Finally we provide guidelines/best practices for researchers utilizing time-based data from Git repositories.