research-article

API compatibility issues in Android: Causes and effectiveness of data-driven detection techniques

Authors:

Simone Scalabrino,

Gabriele Bavota,

Mario Linares-Vásquez,

Valentina Piantadosi,

Rocco OlivetoAuthors Info & Claims

Empirical Software Engineering, Volume 25, Issue 6

Pages 5006 - 5046

https://doi.org/10.1007/s10664-020-09877-w

Published: 01 November 2020 Publication History

Abstract

Android fragmentation is a well-known issue referring to the adoption of different versions in the multitude of devices supporting such an operating system. Each Android version features a set of APIs provided to developers. These APIs are subject to changes and may cause compatibility issues. To support app developers, approaches have been proposed to automatically identify API compatibility issues. CiD, the state-of-the-art approach, is a data-driven solution learning how to detect those issues by analyzing the change history of Android APIs (“API side” learning). In this paper (extension of our MSR 2019 paper), we present an alternative data-driven approach, named ACRyL. ACRyL learns from changes implemented in apps in response to API changes (“client side” learning). When comparing these two solutions on 668 apps, for a total of 11,863 snapshots, we found that there is no clear winner, since the two techniques are highly complementary, and none of them provides a comprehensive support in detecting API compatibility issues: ACRyL achieves a precision of 7.0% (28.0%, when considering only the severe warnings), while CiD achieves a precision of 18.4%. This calls for more research in this field, and led us to run a second empirical study in which we manually analyze 500 pull-requests likely related to the fixing of compatibility issues, documenting the root cause behind the fixed issue. The most common causes are related to changes in the Android APIs (

\sim

87%), while about 13% of the issues are related to external causes, such as build and distribution, dependencies, and the app itself. The provided empirical knowledge can inform the building of better tools for the detection of API compatibility issues.

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Cited By

Liu PZhao YFazzini MCai HGrundy JLi L(2023)Automatically Detecting Incompatible Android APIsACM Transactions on Software Engineering and Methodology10.1145/362473733:1(1-33)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3624737
Fang CYu SSu TZhang JTian YLiu Y(2023)Test Report Generation for Android App Testing Via Heterogeneous Data AnalysisIEEE Transactions on Software Engineering10.1109/TSE.2023.323724749:5(3032-3051)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TSE.2023.3237247
Yang SChen SFan LXu SHui ZHuang SGrundy JPollock LPenta M(2023)Compatibility Issue Detection for Android Apps Based on Path-Sensitive Semantic AnalysisProceedings of the 45th International Conference on Software Engineering10.1109/ICSE48619.2023.00033(257-269)Online publication date: 14-May-2023
https://dl.acm.org/doi/10.1109/ICSE48619.2023.00033
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API compatibility issues in Android: Causes and effectiveness of data-driven detection techniques

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Published In

cover image Empirical Software Engineering

Empirical Software Engineering Volume 25, Issue 6

Nov 2020

1119 pages

ISSN:1382-3256

Issue’s Table of Contents

© Springer Science+Business Media, LLC, part of Springer Nature 2020.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 November 2020

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Cited By

Liu PZhao YFazzini MCai HGrundy JLi L(2023)Automatically Detecting Incompatible Android APIsACM Transactions on Software Engineering and Methodology10.1145/362473733:1(1-33)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3624737
Fang CYu SSu TZhang JTian YLiu Y(2023)Test Report Generation for Android App Testing Via Heterogeneous Data AnalysisIEEE Transactions on Software Engineering10.1109/TSE.2023.323724749:5(3032-3051)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TSE.2023.3237247
Yang SChen SFan LXu SHui ZHuang SGrundy JPollock LPenta M(2023)Compatibility Issue Detection for Android Apps Based on Path-Sensitive Semantic AnalysisProceedings of the 45th International Conference on Software Engineering10.1109/ICSE48619.2023.00033(257-269)Online publication date: 14-May-2023
https://dl.acm.org/doi/10.1109/ICSE48619.2023.00033
Ma TZhao YLi LLiu LBissyandé TKlein JBird CSarro F(2023)CiD4HMOS: A Solution to HarmonyOS Compatibility IssuesProceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE56229.2023.00134(2006-2017)Online publication date: 11-Nov-2023
https://dl.acm.org/doi/10.1109/ASE56229.2023.00134
Rua RSaraiva J(2023)A large-scale empirical study on mobile performance: energy, run-time and memoryEmpirical Software Engineering10.1007/s10664-023-10391-y29:1Online publication date: 27-Dec-2023
https://dl.acm.org/doi/10.1007/s10664-023-10391-y
Cassieri PRomano SScanniello G(2023)On Deprecated API Usages: An Exploratory Study of Top-Starred Projects on GitHubProduct-Focused Software Process Improvement10.1007/978-3-031-49266-2_29(415-431)Online publication date: 11-Dec-2023
https://dl.acm.org/doi/10.1007/978-3-031-49266-2_29
Sun XChen XZhao YLiu PGrundy JLi L(2022)Mining Android API Usage to Generate Unit Test Cases for Pinpointing Compatibility IssuesProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3561151(1-13)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3561151
Mahmud TChe MYang G(2022)Android API Field Evolution and Its Induced Compatibility IssuesProceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3544902.3546242(34-44)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1145/3544902.3546242
Liu PZhao YCai HFazzini MGrundy JLi LRyu SSmaragdakis Y(2022)Automatically detecting API-induced compatibility issues in Android apps: a comparative analysis (replicability study)Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3533767.3534407(617-628)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1145/3533767.3534407

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