PhD Defence Chong Wang | Evolutionary Requirements Elicitation and Analysis Based on Crowdsourced User Feedback | an Approach and Tool Support

Evolutionary Requirements Elicitation and Analysis Based on Crowdsourced User Feedback | an Approach and Tool Support

Chong Wang is a PhD student in the department Semantics, Cybersecurity & Services. (Co)Promotors are prof.dr. G. Guizzardi; dr. ir. M.J. van Sinderen and dr. M. Daneva from the faculty of Electrical Engineering, Mathematics and Computer Sciences (EEMCS), University of Twente.

The rapid expansion of the software market has generated a massive volume of user feedback, providing a rich data source for researchers and practitioners in the requirements engineering (RE) community. This dissertation addresses a critical challenge in the emerging field of Crowdsourced RE for mobile apps, one of the fastest-growing software types: how to effectively leverage this vast user feedback to support software maintenance and evolution.

Unlike traditional methods for RE activities, automated methods are essential in Crowdsourced RE for mobile apps to effectively facilitate the rapid extraction and analysis of requirements from the vast volume of user feedback, enabling timely responses for software maintenance and evolution, especially app updates. It is also valuable to investigate whether these feedback-based requirements are actually considered and implemented in subsequent app iterations. While automating requirements elicitation from app reviews is feasible, few studies have leveraged release notes to enhance requirements extraction and classification, particularly distinguishing between functional and non-functional requirements. Moreover, several studies have explored the links between app reviews and app updates, offering valuable insights into their impact on app evolution and iterative development.

Motivated by these gaps, this paper-based dissertation proposes an approach to enhance automated elicitation and analysis of requirements from crowdsourced user feedback for software maintenance and evolution. Through two systematic mapping studies, we first investigated the state-of-the-art of crowdsourced RE, including user feedback sources, metadata, and prevalent processing and analysis techniques. Based on these findings, we proposed an approach to enhance automated elicitation of evolutionary requirements from app reviews, classify them into functional and non-functional types, analyze the characteristics of app updates, and identify the roles of app reviews in app updates from the developers’ perspectives. The approach was validated through a series of experiments on the constructed research dataset of app reviews and release notes to evaluate its performance and generalizability, and further identify the influence of app reviews on app updates.