API compatibility issues in Android: Causes and effectiveness of data-driven detection techniques
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 su...
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| Published in | Empirical software engineering : an international journal Vol. 25; no. 6; pp. 5006 - 5046 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.11.2020
Springer Nature B.V |
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| Online Access | Get full text |
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| 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 (
∼
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. |
|---|---|
| AbstractList | 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 (∼ 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. 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 ( ∼ 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. |
| Author | Oliveto, Rocco Linares-Vásquez, Mario Lanza, Michele Scalabrino, Simone Bavota, Gabriele Piantadosi, Valentina |
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| Cites_doi | 10.1109/SP.2014.33 10.1109/WCRE.2012.18 10.1145/2950290.2950298 10.1109/ICSME.2016.64 10.1109/SPW.2016.31 10.1145/2597008.2597155 10.1109/ICSM.2013.18 10.1109/MSR.2019.00055 10.1145/3197231.3197255 10.1109/ESEM.2013.9 10.1007/978-3-319-94361-9_10 10.1145/2393596.2393662 10.1145/2491411.2491428 10.1109/MSR.2016.055 10.1109/TSE.2018.2827384 10.1109/SANER.2016.99 10.1145/3196398.3196419 10.1109/ASE.2015.89 10.1109/ICSME.2016.35 10.1109/ICSME.2017.27 10.1109/MOBILESoft.2019.00016 10.1145/2351676.2351722 10.1109/TSE.2014.2367027 10.1145/2970276.2970312 10.1007/978-3-319-60033-8_58 10.1109/ASE.2017.8115644 10.1145/3213846.3213857 10.1145/3238147.3238185 |
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| References_xml | – reference: Han D, Zhang C, Fan X, Hindle A, Wong K, Stroulia E (2012) Understanding android fragmentation with topic analysis of vendor-specific bugs. In: Proceedings of the 19th working conference on reverse engineering. WCRE, pp 83–92 https://doi.org/10.1109/WCRE.2012.18 – reference: Linares-Vásquez M, Bavota G, Bernal-Cárdenas C, Di Penta M, Oliveto R, Poshyvanyk D (2013) API change and fault proneness: A threat to the success of Android apps. In: Proceedings of the 9th Joint meeting on foundations of software engineering. ACM, ESEC/FSE, pp 477–487, https://doi.org/10.1145/2491411.2491428 – reference: Choudhary SR, Gorla A, Orso A (2015) Automated test input generation for Android: Are we there yet?. In: Proceedings of the 30th IEEE/ACM international conference on automated software engineering, IEEE Computer Society, ASE, pp 429–440, https://doi.org/10.1109/ASE.2015.89 – reference: Bartel A, Klein J, Le Traon Y, Monperrus M (2012) Automatically securing permission-based software by reducing the attack surface: An application to android. In: 2012 Proceedings of the 27th IEEE/ACM international conference on automated software engineering. IEEE, pp 274–277 – reference: Robbes R, Lungu M, Röthlisberger D (2012) How do developers react to API deprecation?: The case of a Smalltalk ecosystem. In: Proceedings of the 20th ACM SIGSOFT International Symposium on the Foundations of Software Engineering. ACM, FSE, pp 56:1–56:11 https://doi.org/10.1145/2393596.2393662 – reference: Zhou J, Walker R J (2016) API Deprecation: a retrospective analysis and detection method for code examples on the web. 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| Title | API compatibility issues in Android: Causes and effectiveness of data-driven detection techniques |
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