Article

Temporal analysis of API usage concepts

Authors:

Barthélémy Dagenais,

Martin P. RobillardAuthors Info & Claims

ICSE '12: Proceedings of the 34th International Conference on Software Engineering

Pages 804 - 814

Published: 02 June 2012 Publication History

Abstract

Software reuse through Application Programming Interfaces (APIs) is an integral part of software development. The functionality offered by an API is not always accessed uniformly throughout the lifetime of a client program. We propose Temporal API Usage Pattern Mining to detect API usage patterns in terms of their time of introduction into client programs. We detect concepts as distinct groups of API functionality from the change history of a client program. We locate those concepts in the client change history and detect temporal usage patterns, where a pattern contains a set of concepts that were added into the client program in a specific temporal order. We investigated the properties of temporal API usage patterns through a multiple-case study of three APIs and their use in up to 19 client software projects. Our technique was able to detect a number of valuable patterns in two out of three of the APIs investigated. Further investigation showed some patterns to be relatively consistent between clients, produced by multiple developers, and not trivially derivable from program structure or API documentation.

References

[1]

M. Acharya, T. Xie, J. Pei, and J. Xu. Mining API patterns as partial orders from source code: From usage scenarios to specifications. In Proc. 6th Joint Meeting of the European Software Engineering Conf. and ACM SIGSOFT Symp. on the Foundations of Software Eng., pages 25-34, 2010.

Digital Library

[2]

R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proc. Conf. of the 20th Int. Conf. on Very Large Databases, pages 192-202, 1994.

Digital Library

[3]

O. Alam, B. Adams, and A. E. Hassan. A study of the time dependence of code changes. In Proc. 16th Working Conf. Reverse Eng., pages 21-30, 2009.

Digital Library

[4]

M. Bruch, M. Monperrus, and M. Mezini. Learning from examples to improve code completion systems. In Proc. 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering, pages 213-222, 2009.

Digital Library

[5]

B. Dagenais and L. Hendren. Enabling static analysis for Java programs. In Proc. 23rd Conf. on Object-Oriented Prog. Systems, Languages and Applications, pages 313-328, 2008.

Digital Library

[6]

B. Dagenais and M. P. Robillard. Recommending adaptive changes for framework evolution. In Proc. 30th Intl. Conf. Soft. Eng., pages 481-490, 2008.

Digital Library

[7]

B. Dagenais and M. P. Robillard. Creating and evolving developer documentation: Understanding the decisions of open source contributors. In Proc. 18th Intl. Symp. Foundations of Soft. Eng., pages 127-136, 2010.

Digital Library

[8]

U. Dekel and J. D. Herbsleb. Improving API documentation usability with knowledge pushing. In Proc. 31st Intl. Conf. Soft. Eng., pages 320-330, 2009.

Digital Library

[9]

E. Duala-Ekoko and M. P. Robillard. Using structure-based recommendations to facilitate discoverability in APIs. In Proc. 25th European Conf. Object-Oriented Prog., pages 79-104, 2011.

Digital Library

[10]

R. Holmes, R. J. Walker, and G. C. Murphy. Approximate structural context matching: An approach to recommend relevant examples. IEEE Trans. Soft. Eng., 32(12):952-970, 2006.

Digital Library

[11]

I. Jolliffe. Principal Component Analysis. Springer Series in Statistics, 2nd edition, 2002.

[12]

Z. Li and Y. Zhou. PR-Miner: automatically extracting implicit programming rules and detecting violations in large software code. In Proc. 10th European Soft. Eng. Conf. and Intl. Symp. Foundations Soft. Eng., pages 306-315, 2005.

Digital Library

[13]

D. Lo, S.-C. Khoo, and C. Liu. Mining temporal rules for software maintenance. Journal of Software Maintenance and Evolution: Research and Practice, 20(4):227-247, 2008.

Digital Library

[14]

C. D. Manning, P. Raghavan, and H. Schütze. An Introduction to Information Retrieval. Cambridge University Press, 1st edition, April 2009.

Digital Library

[15]

A. Michail. Data mining library reuse patterns in user-selected applications. In Proc. 14th Intl. Conf. Automated Soft. Eng., pages 24-33, 1999.

Digital Library

[16]

Y. M. Mileva, V. Dallmeier, M. Burger, and A. Zeller. Mining trends of library usage. In Proc. Intl. ERCIM Workshop on Principles of Soft. Evolution and Software Evolution Workshop, pages 57-62, 2009.

Digital Library

[17]

Y. M. Mileva, V. Dallmeier, and A. Zeller. Mining API popularity. In Proc. Conf. of Testing: Academic & Industrial Conf. Practice and Research Techniques, pages 173-180, 2010.

Digital Library

[18]

M. P. Robillard and R. DeLine. A field study of API learning obstacles. Empirical Software Engineering, 2011.

Digital Library

[19]

G. Uddin, B. Dagenais, and M. P. Robillard. Analyzing temporal API usage patterns. In Proc. 26th IEEE/ACM Intl. Conf. on Automated Software Engineering, page 4, http://www.cs.mcgill.ca/~martin/ papers/ase2011.pdf, 2011. To appear.

Digital Library

[20]

R. K. Yin. Case study Research: Design and Methods. Sage, 4th edition, 2009.

[21]

A. T. Ying, G. C. Murphy, R. Ng, and M. C. Chu-Carroll. Predicting source code changes by mining change history. IEEE Trans. Soft. Eng., 30(9):574-586, 2004.

Digital Library

[22]

H. Zhong, T. Xie, L. Zhang, J. Pei, and H. Mei. MAPO: Mining and recommending API usage patterns. In Proc. 23rd European Conf. Object-Oriented Prog., pages 318-343, 2009.

Digital Library

[23]

T. Zimmermann and P. Weißgerber. Preprocessing CVS data for fine-grained analysis. In Proc. 1st Intl. Workshop on Mining Software Repositories, pages 2-6, 2004.

[24]

T. Zimmermann, P. Weißgerber, S. Diehl, and A. Zeller. Mining version histories to guide software changes. IEEE Transactions on Software Engineering, 31(6):429-445, 2005.

Digital Library

Cited By

Islam MPan RNguyen GRajan HRothermel GBae D(2020)Repairing deep neural networksProceedings of the ACM/IEEE 42nd International Conference on Software Engineering10.1145/3377811.3380378(1135-1146)Online publication date: 27-Jun-2020
https://dl.acm.org/doi/10.1145/3377811.3380378
Liu QLiu ZZhu HFan HDu BQian YStorey MAdams BHaiduc S(2019)Generating commit messages from diffs using pointer-generator networkProceedings of the 16th International Conference on Mining Software Repositories10.1109/MSR.2019.00056(299-309)Online publication date: 26-May-2019
https://dl.acm.org/doi/10.1109/MSR.2019.00056
Raelijohn EFamelis MSahraoui HGnesi SPlat NDay NRossi M(2019)A vision for helping developers use APIs by leveraging temporal patternsProceedings of the 7th International Workshop on Formal Methods in Software Engineering10.1109/FormaliSE.2019.00019(95-98)Online publication date: 27-May-2019
https://dl.acm.org/doi/10.1109/FormaliSE.2019.00019
Show More Cited By

Recommendations

Eclipse API usage: the good and the bad

Today, when constructing software systems, many developers build their systems on top of frameworks. Eclipse is such a framework that has been in existence for over a decade. Like many other evolving software systems, the Eclipse platform has both ...
Analyzing temporal API usage patterns
ASE '11: Proceedings of the 26th IEEE/ACM International Conference on Automated Software Engineering

Software reuse through Application Programming Interfaces (APIs) is an integral part of software development. As developers write client programs, their understanding and usage of APIs change over time. Can we learn from long-term changes in how ...
Analyzing the Eclipse API Usage: Putting the Developer in the Loop
CSMR '13: Proceedings of the 2013 17th European Conference on Software Maintenance and Reengineering

Eclipse guidelines distinguish between two types of interfaces provided to third-party developers, i.e., APIs and non-APIs. APIs are stable and supported, while non-APIs are unstable, unsupported and discouraged as they are subject to arbitrary change ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICSE '12: Proceedings of the 34th International Conference on Software Engineering

June 2012

1657 pages

ISBN:9781467310673

General Chair:
Martin Glinz,
Program Chairs:
Gail Murphy,
Mauro Pezzè

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering

Publisher

IEEE Press

Publication History

Published: 02 June 2012

Check for updates

Qualifiers

Article

Conference

ICSE '12

Sponsor:

SIGSOFT

ICSE '12: 34th International Conference on Software Engineering

June 2 - 9, 2012

Zurich, Switzerland

Acceptance Rates

Overall Acceptance Rate 276 of 1,856 submissions, 15%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
307
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Islam MPan RNguyen GRajan HRothermel GBae D(2020)Repairing deep neural networksProceedings of the ACM/IEEE 42nd International Conference on Software Engineering10.1145/3377811.3380378(1135-1146)Online publication date: 27-Jun-2020
https://dl.acm.org/doi/10.1145/3377811.3380378
Liu QLiu ZZhu HFan HDu BQian YStorey MAdams BHaiduc S(2019)Generating commit messages from diffs using pointer-generator networkProceedings of the 16th International Conference on Mining Software Repositories10.1109/MSR.2019.00056(299-309)Online publication date: 26-May-2019
https://dl.acm.org/doi/10.1109/MSR.2019.00056
Raelijohn EFamelis MSahraoui HGnesi SPlat NDay NRossi M(2019)A vision for helping developers use APIs by leveraging temporal patternsProceedings of the 7th International Workshop on Formal Methods in Software Engineering10.1109/FormaliSE.2019.00019(95-98)Online publication date: 27-May-2019
https://dl.acm.org/doi/10.1109/FormaliSE.2019.00019
(2018)API library-based identification and documentation of usage patternsInternational Journal of Computer Applications in Technology10.1504/IJCAT.2018.09406558:1(63-79)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1504/IJCAT.2018.094065
Saied MSahraoui HBatot EFamelis MTalbot PTakadama K(2018)Towards the automated recovery of complex temporal API-usage patternsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205622(1435-1442)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205622
Shatnawi AShatnawi HSaied MShara ZSahraoui HSeriai AKhomh FRoy CSiegmund J(2018)Identifying software components from object-oriented APIs based on dynamic analysisProceedings of the 26th Conference on Program Comprehension10.1145/3196321.3196349(189-199)Online publication date: 28-May-2018
https://dl.acm.org/doi/10.1145/3196321.3196349
Rathee AChhabra J(2017)Restructuring of Object-Oriented Software Through Cohesion Improvement Using Frequent Usage PatternsACM SIGSOFT Software Engineering Notes10.1145/3127360.312737042:3(1-8)Online publication date: 5-Sep-2017
https://dl.acm.org/doi/10.1145/3127360.3127370
Molderez TStevens RDe Roover CGonzalez-Barahona JHindle ATan L(2017)Mining change histories for unknown systematic editsProceedings of the 14th International Conference on Mining Software Repositories10.1109/MSR.2017.12(248-256)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.1109/MSR.2017.12
Huppe SSaied MSahraoui HUchitel SOrso ARobillard M(2017)Mining complex temporal API usage patternsProceedings of the 39th International Conference on Software Engineering Companion10.1109/ICSE-C.2017.147(274-276)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.1109/ICSE-C.2017.147
Fowkes JSutton CZimmermann TCleland-Huang JSu Z(2016)Parameter-free probabilistic API mining across GitHubProceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering10.1145/2950290.2950319(254-265)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1145/2950290.2950319
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.

Figures

Tables

Media

View Table of Conten