Exploring automation of the development of requirements from user feedback
| dc.contributor.author | Li, Ze Shi | |
| dc.contributor.supervisor | Damian, Daniela | |
| dc.contributor.supervisor | Ernst, Neil | |
| dc.date.accessioned | 2025-07-30T20:54:11Z | |
| dc.date.available | 2025-07-30T20:54:11Z | |
| dc.date.issued | 2025 | |
| dc.degree.department | Department of Computer Science | |
| dc.degree.level | Doctor of Philosophy PhD | |
| dc.description.abstract | In modern software development, products and services collect heterogeneous feedback from end users on various platforms such as app stores, social media, forums, and videos. This user feedback is a source for identifying emerging needs, bugs, and potential features. As organizations shift toward rapid release cycles and continuous delivery models, the volume and breadth of user feedback have increased significantly. Traditional requirements elicitation techniques, such as interviews and surveys, remain time-consuming, stakeholder-dependent, and difficult to scale. Moreover, newer mediums like TikTok, YouTube, and Reddit have introduced informal, crowd-driven forms of feedback that are often unstructured and scattered across platforms. This has created a pressing need for scalability and methodological support to analyze and synthesize large-scale user feedback. This dissertation addresses this challenge by exploring scalable, AI-driven approaches to feedback analysis in requirements engineering. For my first research goal, I aimed to investigate how software organizations manage user feedback. I conducted a grounded theory interview study with 40 practitioners from 32 companies to explore how organizations manage user feedback. My analysis identified many feedback channels and activities. Synthesizing these, I propose a life cycle of managing user feedback along with best practices for managing large-scale crowd feedback. For my second research goal, I explored approaches to automate the development of new requirements from user feedback. For textual feedback, such as Reddit and app store reviews, I applied large language models (LLMs) to identify requirements relevant feedback and important themes from the data. This LLM-based approach was more efficient and less laborious than manual analysis for the same purpose. Additionally, I examined automating the analysis of video-based feedback. I extracted transcripts and on-screen text and employed deep learning classifiers to detect requirements-relevant content. My work shows that AI models can identify multimodal user feedback for requirements insights at scale. Given the rising ubiquity of generative AI tools, my third research goal was to explore how we can use such generative AI tools to help automate the development of requirements from user feedback. I began by developing a theory that outlines the factors (i.e., motives and challenges) influencing AI adoption in software teams at both the individual and organizational levels through 26 interviews. Understanding these factors details how generative AI tools could be introduced and supported in practice. Finally, I conducted a think-aloud study with requirements practitioners and product managers to understand how they use generative AI tools during the development of new user requirements from feedback. Participants were observed forming prompts and integrating AI-generated suggestions while analyzing user feedback and formulating requirements. This study highlighted the observed practitioners’ practices. To summarize, this dissertation highlights the findings across all studies, which culminate in a process for AI-assisted development of new requirements from user feedback. This conceptual model synthesizes the lifecycle of user feedback management, automation techniques for multi-modal analysis, and the socio-technical factors shaping tool adoption. This model offers both theoretical and practical contributions by providing scalable, human-centered strategies for transforming crowd-driven user feedback into requirements. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.uri | https://hdl.handle.net/1828/22523 | |
| dc.language | English | eng |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | Requirements engineering | |
| dc.subject | Software engineering | |
| dc.title | Exploring automation of the development of requirements from user feedback | |
| dc.type | Thesis |