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

TL;DR

This paper surveys the use of time-based data in software engineering research, quantifies its prevalence, identifies common data quality issues, and offers guidelines for researchers to improve data accuracy in Git-based studies.

Contribution

It provides the first comprehensive survey of time-based data usage in MSR papers, quantifies data dirtiness sources, and offers best practices for data quality assurance.

Findings

At least 35% of MSR papers use time-based data.

Multiple sources of dirty commit timestamps are identified.

Guidelines for improving data quality in Git research are proposed.

Abstract

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.