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Understanding and Detecting Inefficient Image Displaying Issues in Android Apps

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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 first establishes a descriptive framework for the image displaying procedures of IID issues. Based on the descriptive framework, we conduct an empirical study of 216 real-world IID issues collected from 243 popular open-source Android apps to validate the presence and severity of IID issues, and then shed light on these issues’ characteristics to support research on effective issue detection. With the findings of this study, we propose a static IID issue detection tool TAPIR and evaluate it with 243 real-world Android apps. Encouragingly, 49 and 64 previously-unknown IID issues in two different versions of 16 apps reported by TAPIR are manually confirmed as true positives, respectively, and 16 previously-unknown IID issues reported by TAPIR have been confirmed by developers and 13 have been fixed. Then, we further evaluate the performance impact of these detected IID issues and the performance improvement if they are fixed. The results demonstrate that the IID issues detected by TAPIR indeed cause significant performance degradation, which further show the effectiveness and efficiency of TAPIR.

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Authors and Affiliations

  1. State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Wen-Jie Li  (李文杰), Jun Ma  (马 骏), Yan-Yan Jiang  (蒋炎岩), Chang Xu  (许 畅) & Xiao-Xing Ma  (马晓星)

  2. Department of Computer Science and Technology, Nanjing University, Nanjing, 210023, China

    Wen-Jie Li  (李文杰), Jun Ma  (马 骏), Yan-Yan Jiang  (蒋炎岩), Chang Xu  (许 畅) & Xiao-Xing Ma  (马晓星)

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  1. Wen-Jie Li  (李文杰)
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  3. Yan-Yan Jiang  (蒋炎岩)
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Correspondence to Jun Ma  (马 骏).

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Conflict of Interest The authors declare that they have no conflict of interest.

Additional information

A preliminary version of the paper was published in the Proceedings of SANER 2019.

This work was supported by the Leading-Edge Technology Program of Jiangsu Natural Science Foundation of China under Grant No. BK20202001, and the National Natural Science Foundation of China under Grant No. 61932021. The authors would like to thank the support from the Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China.

Wen-Jie Li is a lecturer with the School of Computer and Information, Anhui Normal University, Wuhu. He received his Ph.D. degree from Nanjing University, Nanjing, in 2022. His research interests include Android application analysis and testing.

Jun Ma is an associate professor in the Department of Computer Science and Technology at Nanjing University, Nanjing. He received his Ph.D. degree from Nanjing University, Nanjing, in 2015. His research interests include software testing and analysis, and software composition analysis.

Yan-Yan Jiang received his Ph.D. degree from Nanjing University, Nanjing, in 2017. He is currently an associate professor in the Department of Computer Science and Technology, Nanjing University, Nanjing. His research interests include software testing/analysis and program synthesis.

Chang Xu is a professor in the Department of Computer Science and Technology at Nanjing University, Nanjing. He received his Ph.D. degree in computer science and engineering from The Hong Kong University of Science and Technology, Hong Kong, in 2008. His research interests include big data software engineering, software testing and analysis, and adaptive and embedded system.

Xiao-Xing Ma received his Ph.D. degree in computer science and technology from Nanjing University, Nanjing, in 2003. He is currently a professor in State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing. His research topics include adaptive software systems, software architectures, middleware systems, and assurance of non-functional software qualities.

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Li, WJ., Ma, J., Jiang, YY. et al. Understanding and Detecting Inefficient Image Displaying Issues in Android Apps. J. Comput. Sci. Technol. 39, 434–459 (2024). https://doi.org/10.1007/s11390-022-1670-3

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  • DOI: https://doi.org/10.1007/s11390-022-1670-3

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