Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews

Alberto Dumont Alves Oliveira D. M. Eler Wajdi Aljedaani + 3 lainnya

19 April 2023

Accessibility reviews collected from app stores may contain valuable information for improving apps accessibility. Recent studies have presented insightful information on accessibility reviews, but they were based on small datasets and focused on general accessibility concerns. In this paper, we analyzed accessibility reviews that report issues affecting users with visual disabilities or conditions. Such reviews were identified based on selection criteria applied over 179,519,598 reviews of popular apps on the Google Play Store. Our results show that only 0,003% of user reviews mention visual disabilities or conditions; accessibility reviews are associated with 36 visual disabilities or eye conditions; many users do not give precise feedback and refer to their disability using generic terms; accessibility reviews can be grouped into general topics of concerns related to different types of disabilities; and positive reviews are generally associated with high scores and negative feedback with lower scores.

S. Malek Navid Salehnamadi S. Branham + 3 lainnya

6 Mei 2021

For 15% of the world population with disabilities, accessibility is arguably the most critical software quality attribute. The ever-growing reliance of users with disability on mobile apps further underscores the need for accessible software in this domain. Existing automated accessibility assessment techniques primarily aim to detect violations of predefined guidelines, thereby produce a massive amount of accessibility warnings that often overlook the way software is actually used by users with disability. This paper presents a novel, high-fidelity form of accessibility testing for Android apps, called Latte, that automatically reuses tests written to evaluate an app’s functional correctness to assess its accessibility as well. Latte first extracts the use case corresponding to each test, and then executes each use case in the way disabled users would, i.e., using assistive services. Our empirical evaluation on real-world Android apps demonstrates Latte’s effectiveness in detecting substantially more useful defects than prior techniques.

A. Perini Jacek Dąbrowski Emmanuel Letier + 1 lainnya

20 Januari 2022

App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.

Qi Shan Samuel White Lilian de Greef + 9 lainnya

13 Januari 2021

Many accessibility features available on mobile platforms require applications (apps) to provide complete and accurate metadata describing user interface (UI) components. Unfortunately, many apps do not provide sufficient metadata for accessibility features to work as expected. In this paper, we explore inferring accessibility metadata for mobile apps from their pixels, as the visual interfaces often best reflect an app’s full functionality. We trained a robust, fast, memory-efficient, on-device model to detect UI elements using a dataset of 77,637 screens (from 4,068 iPhone apps) that we collected and annotated. To further improve UI detections and add semantic information, we introduced heuristics (e.g., UI grouping and ordering) and additional models (e.g., recognize UI content, state, interactivity). We built Screen Recognition to generate accessibility metadata to augment iOS VoiceOver. In a study with 9 screen reader users, we validated that our approach improves the accessibility of existing mobile apps, enabling even previously inaccessible apps to be used.

S. Malek Ziyao He Syed Fatiul Huq + 1 lainnya

19 April 2023

It is crucial to make software, with its ever-growing influence on everyday lives, accessible to all, including people with disabilities. Despite promoting software accessibility through government regulations, development guidelines, tools and frameworks, investigations reveal a marketplace of inaccessible web and mobile applications. To better understand the limitations of contemporary software industry in adopting accessibility practices, it is necessary to construct a holistic view that combines the perspectives of software practitioners, stakeholders and end users. In this paper, we collect 637 conversations from Twitter to synthesize and qualitatively analyze discussions posted about software accessibility. Our findings observe an active community that provides feedback on inaccessible software, shares personal accounts of development practices and advocates for inclusivity. By perceiving software accessibility from process, profession and people viewpoints, we present current conventions, challenges and possible resolutions with four emergent themes: cost and incentives, awareness and advocacy, technology and resources, and integration and inclusion.