On the Automated Processing of User Feedback

TL;DR

This paper reviews techniques using data mining, machine learning, and NLP, including Large Language Models, to analyze large, variable-quality user feedback for software engineering improvements.

Contribution

It provides a comprehensive overview of methods to process and analyze user feedback, addressing challenges of data volume and quality with recent AI techniques.

Findings

Effective pipelines for user feedback analysis are summarized.

Recent NLP and Large Language Models enhance feedback processing.

Guidelines for practitioners and researchers are provided.

Abstract

User feedback is becoming an increasingly important source of information for requirements engineering, user interface design, and software engineering in general. Nowadays, user feedback is largely available and easily accessible in social media, product forums, or app stores. Over the last decade, research has shown that user feedback can help software teams: a) better understand how users are actually using specific product features and components, b) faster identify, reproduce, and fix defects, and b) get inspirations for improvements or new features. However, to tap the full potential of feedback, there are two main challenges that need to be solved. First, software vendors must cope with a large quantity of feedback data, which is hard to manage manually. Second, vendors must also cope with a varying quality of feedback as some items might be uninformative, repetitive, or simply wrong. This chapter summarises and pipelines various data mining, machine learning, and natural language processing techniques, including recent Large Language Models, to cope with the quantity and quality challenges. We guide researchers and practitioners through implementing effective, actionable analysis of user feedback for software and requirements engineering.