In recent years, mobile accessibility has become an important trend with the goal of allowing all users the possibility of using any app without many restrictions. Recent work demonstrated that user reviews include insights that are useful for app evolution. However, with the increase in the amount of received reviews, manually analyzing them is tedious and time-consuming, especially when searching for accessibility reviews. The goal of this thesis is to support the automated identification of accessibility in user reviews, to help practitioners in prioritizing their handling, and thus, creating more inclusive apps. Particularly, we design a model that takes as input accessibility user reviews, learns their keyword-based features, in order to make a binary decision, for a given review, on whether it is about accessibility or not. The model is evaluated using a total of 5326 mobile app reviews. The findings show that (1) our approach can accurately identify accessibility reviews, outperforming two baselines, namely keyword-based detector and a random classifier; (2) our model achieves F1-measure of 90.7\% with relatively small training dataset; however, F1-measure value improves as we add to the training dataset.

Library of Congress Subject Headings

Mobile apps--Evaluation; Mobile apps--Public opinion; User-centered system design; Accessible Web sites for people with disabilities

Publication Date


Document Type


Student Type


Degree Name

Software Engineering (MS)

Department, Program, or Center

Software Engineering (GCCIS)


Mohamed Wiem Mkaouer

Advisor/Committee Member

Yasmine Elglaly

Advisor/Committee Member

Zhe Yu


RIT – Main Campus

Plan Codes