To appear in Alessio Ferrari and Gouri G. Deshpande (Eds): Handbook of Natural Language Processing for Requirements Engineering, Springer 2024. Preprint of accepted version (authors' version)
用户反馈正成为需求工程、用户界面设计以及整个软件工程中日益重要的信息来源。如今，用户反馈在社交媒体、产品论坛或应用商店中大量存在且易于获取。过去十年间，研究表明用户反馈能帮助软件团队：a) 更好地理解用户实际如何使用特定产品功能和组件；b) 更快地识别、复现和修复缺陷；以及 b) 获得改进或新功能的灵感。然而，要充分发挥反馈的潜力，需要解决两个主要挑战。首先，软件供应商必须应对大量反馈数据，这些数据难以手动管理。其次，供应商还必须应对反馈质量参差不齐的问题，因为某些条目可能信息量不足、重复或完全错误。本章总结并整合了各种数据挖掘、机器学习和自然语言处理技术，包括近期的大语言模型，以应对数量和质量挑战。我们指导研究人员和实践者实施对用户反馈进行有效、可操作的分析，以支持软件和需求工程。
英文摘要：
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.
Can Automated Feedback Turn Students into Happy Prologians?
Universidade de Lisboa（里斯本大学）；Artificial Intelligence Research Institute, Consejo Superior de Investigaciones Científicas（西班牙高等科学研究理事会人工智能研究所）；Carnegie Mellon University（卡内基梅隆大学）
RITA: A Tool for Automated Requirements Classification and Specification from Online User Feedback
Lero, the Research Ireland Centre for Software, University of Limerick（利默里克大学软件研究爱尔兰研究中心）；University College Dublin (UCD)（都柏林大学学院）；University of Padova（帕多瓦大学）
Automated Visualization Code Synthesis via Multi-Path Reasoning and Feedback-Driven Optimization
AI Research, Enhans, Seoul, South Korea Innovation \& Technology, KAIST, Daejeon, South Korea Department of Computer Science, University of California, Berkeley, CA, United States Department of Electrical
RePair: Automated Program Repair with Process-based Feedback
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence Comprehensive National Science Center（国家人工智能综合研究中心）；School of Computer Science and Artificial Intelligence, Hefei Normal University（合肥师范学院计算机与人工智能学院）
