Can User Feedback Help Issue Detection? An Empirical Study on a One-billion-user Online Service System

TL;DR

This study analyzes vast user feedback data from a large online service to understand its characteristics and evaluate machine learning methods for detecting severe issues, highlighting challenges and potential solutions.

Contribution

It provides an empirical analysis of user feedback in large-scale systems and assesses the feasibility of machine learning for issue detection based on feedback features.

Findings

Many feedback items are irrelevant to issues

Severe issues are hard to detect from feedback alone

Feedback topic distributions remain stable over time

Abstract

Background: It has long been suggested that user feedback, typically written in natural language by end-users, can help issue detection. However, for large-scale online service systems that receive a tremendous amount of feedback, it remains a challenging task to identify severe issues from user feedback. Aims: To develop a better feedback-based issue detection approach, it is crucial first to gain a comprehensive understanding of the characteristics of user feedback in real production systems. Method: In this paper, we conduct an empirical study on 50,378,766 user feedback items from six real-world services in a one-billion-user online service system. We first study what users provide in their feedback. We then examine whether certain features of feedback items can be good indicators of severe issues. Finally, we investigate whether adopting machine learning techniques to analyze user feedback is reasonable. Results: Our results show that a large proportion of user feedback provides irrelevant information about system issues. As a result, it is crucial to filter out issue-irrelevant information when processing user feedback. Moreover, we find severe issues that cannot be easily detected based solely on user feedback characteristics. Finally, we find that the distributions of the feedback topics in different time intervals are similar. This confirms that designing machine learning-based approaches is a viable direction for better analyzing user feedback. Conclusions: We consider that our findings can serve as an empirical foundation for feedback-based issue detection in large-scale service systems, which sheds light on the design and implementation of practical issue detection approaches.