Issue Link Label Recovery and Prediction for Open Source Software

TL;DR

This paper investigates automatic classification of issue link types in open source projects using machine learning, demonstrating promising results and highlighting the importance of historical data for future link prediction.

Contribution

It introduces a data-driven approach for automatically recovering and predicting issue link labels, addressing a gap in existing methods that only detect link existence.

Findings

F1 scores between 0.56 and 0.70 for link label recovery

Effective link label prediction with sufficient historical data

First systematic approach for issue link label management

Abstract

Modern open source software development heavily relies on the issue tracking systems to manage their feature requests, bug reports, tasks, and other similar artifacts. Together, those "issues" form a complex network with links to each other. The heterogeneous character of issues inherently results in varied link types and therefore poses a great challenge for users to create and maintain the label of the link manually. The goal of most existing automated issue link construction techniques ceases with only examining the existence of links between issues. In this work, we focus on the next important question of whether we can assess the type of issue link automatically through a data-driven method. We analyze the links between issues and their labels used the issue tracking system for 66 open source projects. Using three projects, we demonstrate promising results when using supervised machine learning classification for the task of link label recovery with careful model selection and tuning, achieving F1 scores of between 0.56-0.70 for the three studied projects. Further, the performance of our method for future link label prediction is convincing when there is sufficient historical data. Our work signifies the first step in systematically manage and maintain issue links faced in practice.