Abstract
Today, mobile devices provide support to disabled people to make their life easier due to their high accessibility and capability, e.g., finding accessible locations, picture and voice-based communication, customized user interfaces and vocabulary levels. These accessibility frameworks are directly integrated, as libraries, in various apps, providing them with accessibility functions. Just like any other software, these frameworks regularly encounter errors. These errors are reported by app developers in the form of bug reports. These bug reports related to accessibility faults need to be urgently fixed since their existence significantly hinders the usability of apps. In this context, the manual inspection of a large number of bug reports to identify accessibility-related ones is time-consuming and error-prone. Prior research has investigated mobile app user reviews classification for various purposes, including bug reports identification, feature request identification, app performance optimization etc. Yet, none of the prior research has investigated the identification of accessibility-related bug reports, making their prioritization and timely correction difficult for software developers. To support developers with this manual process, the goal of this paper is to automatically detect, for a given bug report, whether it is about accessibility or not. Thus, we tackle the identification of accessibility bug reports as a binary classification problem. To build our model, we rely on an existing dataset of manually curated accessibility bug reports, extracted from popular open-source projects, namely Mozilla Firefox and Google Chromium. We design our solution to learn from these reports the appropriate discriminative features i.e., keywords that properly represent accessibility issues. Our trained model is evaluating using stratified cross-validation, and the findings show that our classifier achieves high Fl-scores of 93%.