Supporting Developers in Addressing Human-centric Issues in Mobile Apps

TL;DR

This paper investigates human-centric issues in mobile apps by analyzing user reviews and developer discussions, creating a taxonomy, and developing machine learning models to automatically detect these issues, with a survey indicating practical usefulness.

Contribution

It introduces a taxonomy of human-centric issues, analyzes real-world data from app reviews and GitHub, and develops models for automatic detection of such issues in mobile app development.

Findings

Identified three main categories of human-centric issues: App Usage, Inclusiveness, User Reaction.

Developed machine learning models that show promise in automatically classifying human-centric issues.

Survey indicates developers find automated detection of human-centric issues practically useful.

Abstract

Failure to consider the characteristics, limitations, and abilities of diverse end-users during mobile apps development may lead to problems for end-users such as accessibility and usability issues. We refer to this class of problems as human-centric issues. Despite their importance, there is a limited understanding of the types of human-centric issues that are encountered by end-users and taken into account by the developers of mobile apps. In this paper, we examine what human-centric issues end-users report through Google App Store reviews, which human-centric issues are a topic of discussion for developers on GitHub, and whether end-users and developers discuss the same human-centric issues. We then investigate whether an automated tool might help detect such human-centric issues and whether developers would find such a tool useful. To do this, we conducted an empirical study by extracting and manually analysing a random sample of 1,200 app reviews and 1,200 issue comments from 12 diverse projects that exist on both Google App Store and GitHub. Our analysis led to a taxonomy of human-centric issues that categorises human-centric issues into three-high levels: App Usage, Inclusiveness, and User Reaction. We then developed machine learning and deep learning models that are promising in automatically identifying and classifying human-centric issues from app reviews and developer discussions. A survey of mobile app developers shows that the automated detection of human-centric issues has practical applications. Guided by our findings, we highlight some implications and possible future work to further understand and incorporate human-centric issues in mobile apps development.