research-article

API change and fault proneness: a threat to the success of Android apps

Authors:

Mario Linares-Vásquez,

Gabriele Bavota,

Carlos Bernal-Cárdenas,

Massimiliano Di Penta,

Denys PoshyvanykAuthors Info & Claims

ESEC/FSE 2013: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

Pages 477 - 487

https://doi.org/10.1145/2491411.2491428

Published: 18 August 2013 Publication History

Abstract

During the recent years, the market of mobile software applications (apps) has maintained an impressive upward trajectory. Many small and large software development companies invest considerable resources to target available opportunities. As of today, the markets for such devices feature over 850K+ apps for Android and 900K+ for iOS. Availability, cost, functionality, and usability are just some factors that determine the success or lack of success for a given app. Among the other factors, reliability is an important criteria: users easily get frustrated by repeated failures, crashes, and other bugs; hence, abandoning some apps in favor of others.

This paper reports a study analyzing how the fault- and change-proneness of APIs used by 7,097 (free) Android apps relates to applications' lack of success, estimated from user ratings. Results of this study provide important insights into a crucial issue: making heavy use of fault- and change-prone APIs can negatively impact the success of these apps.

References

[1]

M. Assaduzzaman, M. Bullock, C. Roy, and K. Schneider. Bug Introducing Changes: A Case Study with Android. In Proceedings of the 9th IEEE Working Conference on Mining Software Repositories, Zurich, Switzerland, pp. 116–119. IEEE Press, 2012.

[2]

C. Bird, A. Bachmann, E. Aune, J. Duffy, A. Bernstein, V. Filkov, and P. T. Devanbu. Fair and Balanced?: Bias in Bug-fix Datasets. In Proceedings of the 7th joint meeting of the European Software Engineering Conference and the ACM SIGSOFT International Symposium on Foundations of Software Engineering, Amsterdam, The Netherlands, pp. 121–130. ACM Press, 2009.

Digital Library

[3]

M. L. Collard, H. H. Kagdi, and J. I. Maletic. An XML-based Lightweight C++ Fact Extractor. In Proceedings of the 11th International Workshop on Program Comprehension, Portland, Oregon, USA, pp. 134–143. IEEE Computer Society Press, 2003.

Digital Library

[4]

W. J. Conover. Practical Nonparametric Statistics. Wiley, 3rd edition edition, 1998.

[5]

B. Dagenais and M. P. Robillard. Recommending Adaptive Changes for Framework Evolution. In Proceedings of the 30th International Conference on Software Engineering, Leipzig, Germany, pp. 481–490. ACM Press, 2008.

Digital Library

[6]

J. Davies, D. M. German, M. W. Godfrey, and A. Hindle. Software Bertillonage Determining the Provenance of Software Development Artifacts. Empirical Software Engineering Journal, 2012.

[7]

J. Davies, D. M. German, M. W. Godfrey, and A. J. Hindle. Software Bertillonage: Finding the Provenance of an Entity. In Proceedings of the 8th IEEE Working Conference on Mining Software Repositories, Honolulu, Hawaii, pp. 183–192. IEEE Press, 2011.

Digital Library

[8]

D. Dig and R. Johnson. How Do APIs Evolve? A Story of Refactoring. Journal of Software Maintenance and Evolution: Research and Practice, 18:83–107, 2006.

Digital Library

[9]

A. Dresnos. Android : Static Analysis using Similarity Distance. In Proceedings of the 45th Hawaii International Conference on System Sciences, pp. 5394–5403, 2012.

Digital Library

[10]

M. Fischer, M. Pinzger, and H. Gall. Populating a Release History Database from Version Control and Bug Tracking Systems. In Proceedings of the 19th International Conference on Software Maintenance, Amsterdam, The Netherlands, pp. 23–32. IEEE Press, 2003.

Digital Library

[11]

R. J. Grissom and J. J. Kim. Effect Sizes for Research: A Broad Practical Approach. Lawrence Earlbaum Associates, 2nd edition, 2005.

[12]

S. Holm. A Simple Sequentially Rejective Bonferroni Test Procedure. Scandinavian Journal on Statistics, 6:65–70, 1979.

[13]

D. Hou and X. Yao. Exploring the Intent Behind API Evolution: A Case Study. In Proceedings of the 18th Working Conference on Reverse Engineering, Limerick, Ireland, pp. 131–140. IEEE Press, 2011.

Digital Library

[14]

L. Martie, V. Palepu, H. Sajnani, and C. Lopes. Trendy Bugs: Topic Trends in the Android Bug Reports. In Proceedings of the 9th IEEE Working Conference on Mining Software Repositories, Zurich, Switzerland, pp. 120–123. IEEE Press, 2012.

[15]

J. Mayrand, C. Leblanc, and E. Merlo. Experiment on the Automatic Detection of Function Clones in a Software System using Metrics. In Proceedings of the 12th International Conference on Software Maintenance, Monterey, CA, USA, pp. 244–253. IEEE Computer Society Press, 1996.

Digital Library

[16]

Y. Mileva, V. Dallmeier, M. Burger, and A. Zeller. Mining Trends of Library Usage. In Proceedings of the Joint International and Annual ERCIM Workshops on Principles of Software Evolution and Software Evolution Workshops, Amsterdam, The Netherlands, pp. 57–62. IEEE 2009.

Digital Library

[17]

I. Mojica Ruiz, M. Nagappan, B. Adams, and A. Hassan. Understanding Reuse in the Android Market. In Proceedings of the 20th IEEE International Conference on Program Comprehension, Passau, Bavaria, Germany, pp. 113–122. IEEE Press 2012.

[18]

S. Raemaekers, A. van Deursen, and J. Visser. Measuring Software Library Stability through Historical Version Analysis. In Proceedings of the 8th IEEE International Conference on Software Maintenance, Riva del Garda, Trento, Italy, pp. 378–387. IEEE Press 2012.

Digital Library

[19]

M. Robillard and R. DeLine. A Field Study of API Learning Obstacles. Empirical Software Engineering Journal, 16:703–732, 2012.

Digital Library

[20]

M. P. Robillard and G. C. Murphy. Designing Robust Java Programs with Exceptions. In Proceedings of the 8th ACM SIGSOFT International Symposium on Foundations of Software Engineering: Twenty-first Century Applications, San Diego, CA, USA, pp. 2–10. ACM Press 2000.

Digital Library

[21]

B. Sanz, I. Santos, C. Laorden, X. Ugarte-Pedrero, and P. Bringas. On the Automatic Categorization of Android Applications. In Proceedings of the 2012 IEEE Consumer Communications and Networking Conference, Las Vegas, Nevada, USA, pp. 149–153. IEEE Press 2012.

[22]

A. Shabtai, Y. Fledel, and Y. Elovici. Automated Static Code Analysis for Classifying Android Applications using Machine Learning. In Proceedings of the 2010 International Conference on Computational Intelligence and Security, Nanning, China, pp. 329–333. CPS, 2010.

Digital Library

[23]

E. Shihab, Y. Kamei, and P. Bhattacharya. Mining Challenge 2012: The Android Platform. In Proceedings of the 9th IEEE Working Conference on Mining Software Repositories, Zurich, Switzerland, pp. 112–115. IEEE Press, 2012.

[24]

V. Sinha, S. Mani, and M. Gupta. Mince: Mining Change History of Android Project. In Proceedings of the 9th IEEE Working Conference on Mining Software Repositories, Zurich, Switzerland, pp. 132–135. IEEE Press, 2012.

[25]

D. Syer, B. Adams, Y. Zou, and A. Hassan. Exploring the Development of Micro-Apps: A Case Study on the Blackberry and Android Platforms. In Proceedings of the 11th IEEE International Working Conference on Source Code Analysis and Manipulation, Williamsburg, VA, USA, pp. 55–64. IEEE Press, 2011.

Digital Library

[26]

VisionMobile. The New Mobile App Economy (Developer Economics 2012), 2012.

[27]

VisionMobile. Developer Tools: The Foundations of the App Economy (Developer Economics 2013), 2013.

[28]

M. Zibran. What Makes APIs Diﬃcult to Use? International Journal of Computer Science and Network Security, 8(4):255–261, 2008.

[29]

M. Zibran, F. Eishita, and C. Roy. Useful, but Usable? Factors Affecting the Usability of APIs. In Proceedings of the 18th Working Conference on Reverse Engineering, Limerick, Ireland, pp. 151–155. IEEE Press, 2011.

Digital Library

Cited By

Yang SHou QLi SXu FDiao W(2025)From guidelines to practice: assessing Android app developer compliance with google’s security recommendationsEmpirical Software Engineering10.1007/s10664-024-10559-030:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10664-024-10559-0
Salma SMansur SZhang YMoran KSpinellis DConstantinou EBacchelli A(2024)GuiEvo: Automated Evolution of Mobile App UIsProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644936(335-347)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644936
Barzolevskaia ABranca EStakhanova N(2024)Measuring and Characterizing (Mis)compliance of the Android Permission SystemIEEE Transactions on Software Engineering10.1109/TSE.2024.336292150:4(742-764)Online publication date: Apr-2024
https://doi.org/10.1109/TSE.2024.3362921
Show More Cited By

Index Terms

API change and fault proneness: a threat to the success of Android apps
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
        Software maintenance
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues

Recommendations

Supporting evolution and maintenance of Android apps
ICSE Companion 2014: Companion Proceedings of the 36th International Conference on Software Engineering

In recent years, the market of mobile software applications (apps) has maintained an impressive upward trajectory. As of today, the market for such devices features over 850K+ apps for Android, and 19 versions of the Android API have been released in 4 ...
The Impact of API Change- and Fault-Proneness on the User Ratings of Android Apps
The mobile apps market is one of the fastest growing areas in the information technology. In digging their market share, developers must pay attention to building robust and reliable apps. In fact, users easily get frustrated by repeated failures, crashes,...
Analyzing GUI running fluency for Android apps
MSCC '16: Proceedings of the 3rd ACM Workshop on Mobile Sensing, Computing and Communication

Android as a free open platform has become increasingly popular and been widespread adopted in mobile, tablet, and other devices. However, a great number of issues, such as inadequate quality and the fragmentation phenomenon, have emerged, enhancing the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ESEC/FSE 2013: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

August 2013

738 pages

ISBN:9781450322379

DOI:10.1145/2491411

General Chair:
Bertrand Meyer
ETH Zurich, Switzerland
,
Program Chairs:
Luciano Baresi
Politecnico di Milano, Italy
,
Mira Mezini
TU Darmstadt, Germany

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 August 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ESEC/FSE'13

Sponsor:

SIGSOFT

ESEC/FSE'13: Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering

August 18 - 26, 2013

Saint Petersburg, Russia

Acceptance Rates

Overall Acceptance Rate 112 of 543 submissions, 21%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

192
Total Citations
View Citations
1,227
Total Downloads

Downloads (Last 12 months)32
Downloads (Last 6 weeks)3

Reflects downloads up to 09 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yang SHou QLi SXu FDiao W(2025)From guidelines to practice: assessing Android app developer compliance with google’s security recommendationsEmpirical Software Engineering10.1007/s10664-024-10559-030:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10664-024-10559-0
Salma SMansur SZhang YMoran KSpinellis DConstantinou EBacchelli A(2024)GuiEvo: Automated Evolution of Mobile App UIsProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644936(335-347)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644936
Barzolevskaia ABranca EStakhanova N(2024)Measuring and Characterizing (Mis)compliance of the Android Permission SystemIEEE Transactions on Software Engineering10.1109/TSE.2024.336292150:4(742-764)Online publication date: Apr-2024
https://doi.org/10.1109/TSE.2024.3362921
Yang SBai GLin RGuo JDiao W(2024)Beyond the Horizon: Exploring Cross-Market Security Discrepancies in Parallel Android Apps2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE62328.2024.00059(558-569)Online publication date: 28-Oct-2024
https://doi.org/10.1109/ISSRE62328.2024.00059
Khan SWang WRen YZhu BShi JMcGowan ALam WMoran K(2024)Aurora: Navigating UI Tarpits via Automated Neural Screen Understanding2024 IEEE Conference on Software Testing, Verification and Validation (ICST)10.1109/ICST60714.2024.00028(221-232)Online publication date: 27-May-2024
https://doi.org/10.1109/ICST60714.2024.00028
Malavolta IStoico VLago P(2024)Ten Years of Teaching Empirical Software Engineering in the Context of Energy-Efficient SoftwareHandbook on Teaching Empirical Software Engineering10.1007/978-3-031-71769-7_8(209-253)Online publication date: 25-Dec-2024
https://doi.org/10.1007/978-3-031-71769-7_8
Bibi NRana TMaqbool AAfzal FAkgül ADe la Sen M(2023)An Intelligent Platform for Software Component Mining and RetrievalSensors10.3390/s2301052523:1(525)Online publication date: 3-Jan-2023
https://doi.org/10.3390/s23010525
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
Chen ZShao ZAamodt TJerger NSwift M(2023)Transparent Runtime Change Handling for Android AppsProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 310.1145/3582016.3582060(756-770)Online publication date: 25-Mar-2023
https://dl.acm.org/doi/10.1145/3582016.3582060
Mahmud TChe MYang G(2023)Detecting Android API Compatibility Issues With API DifferencesIEEE Transactions on Software Engineering10.1109/TSE.2023.327415349:7(3857-3871)Online publication date: Jul-2023
https://doi.org/10.1109/TSE.2023.3274153
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents