Entering the new decade, accompanied by the ever-advancing information technology, software products have been penetrating across people’s lives more than ever supporting work, easing communication, encouraging consumption, and providing entertainment. The gigantic software market is still expanding rapidly and is filled with fierce competition.
“The end users of software products play an increasingly important role, because on the growing online distribution platforms they can express freely their opinions about the software. Their various and continuously changing needs become heard this way,” Xiaozhou Li explains.
For software companies, user reviews are both valuable sources for detecting customer needs and effective ways to engage users in product development. The information from the reviews helps companies develop their software products and business. User reviews can be analyzed using data mining, natural language processing techniques and statistical methods.
“Therefore, a method is needed to extract key opinions from a large number of end-user reviews, and by which to analyze changes in opinions over time,” Li says.
Thanks to the advance in the data mining and natural language processing technology, understanding the collective opinions from large volume of texts is much easier.
“Many studies have explored the effective ways of applying such techniques in analyzing software user reviews. However, few have applied the methods to support the software maintenance and evolution towards monitoring the end users’ changing needs,” Li says.
The purpose of Li’s research is to propose a data-driven user review analysis approach to support the continuous understanding of users’ collective needs within the process of software maintenance and evolution.
The research answers the following key research questions: 1) How to analyze users’ collective expectation and perceived quality in use with data-driven approaches by exploiting sentiment and topics? 2) How to monitor user satisfaction over software updates during software evolution using reviews’ topics and sentiments? and 3) How to analyze users’ profiles, software types and situational contexts as contexts of use that supports the analysis of user satisfaction?
“My main implication is to enrich the existing domain knowledge of software maintenance and evolution in terms of taking advantage of the collective intelligence of end users,” Li states.
The doctoral dissertation of MSc (Software Development; Internet & Game Studies) Xiaozhou Li in the field of information and systems titled Data-driven Analysis towards Monitoring Software Evolution by Continuously Understanding Changes in Users’ Needs will be publicly examined in the Faculty of Information Technology and Communication Sciences at Tampere University at 12 o’clock on Thursday 17 February 2022. Professor Filippo Lanubile from University of Bari, Italy, will be the opponent while Adjunct Professor Zheying Zhang will act as the custos.
The dissertation is available online at https://urn.fi/URN:ISBN:978-952-03-2301-1
Due to the COVID-19 situation, the event can be joined via remote connection (Zoom).