Pitfalls and Guidelines for Using Time-Based Git Data

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 aect 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

ABSTRACT Enriching Revision History with Interactions

Revision history provides a rich source of information to improve the understanding of changes made to programs, but it yields only limited insight into how these changes occurred. We explore an additional source of information – program viewing and editing history – where all historical artifacts associated with the program are included. In particular, we suggest augmenting revision histories with the interaction history of programmers. Using this additional information source enables the development of several interesting applications including an influence-recommendation system and a task-mining system. We present some results from a case study in which interaction histories from professional programmers were obtained and analyzed