2019Unpublished venueRequires access

Characterizing and Detecting Inefficient Image Displaying Issues in Android Apps

Wenjie Li, Yanyan Jiang, Chang Xu, Yepang Liu, Xiaoxing Ma, Jian Lü

Open publisher page 23 citations

Abstract

Mobile applications (apps for short) often need to display images. However, inefficient image displaying (IID) issues are pervasive in mobile apps, and can severely impact app performance and user experience. This paper presents an empirical study of 162 real-world IID issues collected from 243 popular open-source Android apps, validating the presence and severity of IID issues, and then sheds light on these issues' characteristics to support future research on effective issue detection. Based on the findings of this study, we developed a static IID issue detection tool TAPIR and evaluated it with real-world Android apps. The experimental evaluations show encouraging results: TAPIR detected 43 previously-unknown IID issues in the latest version of the 243 apps, 16 of which have been confirmed by respective developers and 13 have been fixed.

About this research paper

What this paper is about

Mobile applications (apps for short) often need to display images. However, inefficient image displaying (IID) issues are pervasive in mobile apps, and can severely impact app performance and user experience. This paper presents an empirical study of 162 real-world IID issues collected from 243 popular open-source Android apps, validating the presence and severity of IID issues, and then sheds light on these issues' characteristics to support future research on effective issue detection. Based on the findings of this study, we developed a static IID issue detection tool TAPIR and evaluated it with real-world Android apps. The experimental evaluations show encouraging results: TAPIR detected 43 previously-unknown IID issues in the latest version of the 243 apps, 16 of which have been confirmed by respective developers and 13 have been fixed.

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OpenAlex reports 23 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Mobile applications (apps for short) often need to display images. However, inefficient image displaying (IID) issues are pervasive in mobile apps, and can severely impact app performance and user experience. This paper presents an empirical study of 162 real-world IID issues collected from 243 popular open-source Android apps, validating the presence and severity of IID issues, and then sheds light on these issues' characteristics to support future research on effective issue detection. Based on the findings of this study, we developed a static IID issue detection tool TAPIR and evaluated it with real-world Android apps. The experimental evaluations show encouraging results: TAPIR detected 43 previously-unknown IID issues in the latest version of the 243 apps, 16 of which have been confirmed by respective developers and 13 have been fixed.

Key concepts: Android (operating system), Computer science, Mobile apps, Android app, Empirical research, Android application, Open research, Mobile device

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