Wayback Machine: A tool to capture the evolutionary behaviour of the bug reports and their triage process in open-source software systems

TL;DR

This paper introduces the ITS Wayback Machine, a tool that reconstructs the historical decision-making process in bug triage and prioritization in open-source projects, enabling analysis of bug evolution and triage strategies over time.

Contribution

It presents a novel approach to reconstruct and analyze the evolution of bug reports and triage decisions, addressing subjectivity and inaccuracies in traditional bug tracking data.

Findings

Reveals overlooked evolutionary metrics like overdue bugs and developer load.

Demonstrates effectiveness using data from three open-source projects.

Enables application of various triage algorithms retrospectively.

Abstract

The issue tracking system (ITS) is a rich data source for data-driven decision-making. Different characteristics of bugs, such as severity, priority, and time to fix, provide a clear picture of an ITS. Nevertheless, such information may be misleading. For example, the exact time and the effort spent on a bug might be significantly different from the actual reporting time and the fixing time. Similarly, these values may be subjective, e.g., severity and priority values are assigned based on the intuition of a user or a developer rather than a structured and well-defined procedure. Hence, we explore the evolution of the bug dependency graph together with priority and severity levels to explore the actual triage process. Inspired by the idea of the "Wayback Machine" for the World Wide Web, we aim to reconstruct the historical decisions made in the ITS. Therefore, any bug prioritization or bug triage algorithms/scenarios can be applied in the same environment using our proposed ITS Wayback Machine. More importantly, we track the evolutionary metrics in the ITS when a custom triage/prioritization strategy is employed. We test the efficiency of the proposed algorithm using data extracted from three open-source projects. Our empirical study sheds light on the overlooked evolutionary metrics--e.g., overdue bugs and developers' loads--which are facilitated via our proposed past-event re-generator.