research-article

Can we predict types of code changes?: an empirical analysis

Authors:

Martin Pinzger,

Harald C. GallAuthors Info & Claims

MSR '12: Proceedings of the 9th IEEE Working Conference on Mining Software Repositories

Pages 217 - 226

Published: 02 June 2012 Publication History

Abstract

There exist many approaches that help in pointing developers to the change-prone parts of a software system. Although beneficial, they mostly fall short in providing details of these changes. Fine-grained source code changes (SCC) capture such detailed code changes and their semantics on the statement level. These SCC can be condition changes, interface modifications, inserts or deletions of methods and attributes, or other kinds of statement changes. In this paper, we explore prediction models for whether a source file will be affected by a certain type of SCC. These predictions are computed on the static source code dependency graph and use social network centrality measures and object-oriented metrics. For that, we use change data of the Eclipse platform and the Azureus 3 project. The results show that Neural Network models can predict categories of SCC types. Furthermore, our models can output a list of the potentially change-prone files ranked according to their change-proneness, overall and per change type category.

References

[1]

W. Li and S. Henry, "Object-oriented metrics that predict maintainability," Journal of Systems and Software, vol. 23, no. 2, pp. 111--122, 1993.

Digital Library

[2]

M. Dagpinar and J. Jahnke, "Predicting maintainability with object-oriented metrics - an empirical comparison," in Proc. Working Conf. on Reverse Eng., 2003, pp. 155--164.

Digital Library

[3]

E. Arisholm, L. Briand, and A. Foyen, "Dynamic coupling measurement for object-oriented software," IEEE Trans. Softw. Eng., vol. 30, no. 8, pp. 491--506, 2004.

Digital Library

[4]

T. Girba, S. Ducasse, and M. Lanza, "Yesterday's weather: Guiding early reverse engineering efforts by summarizing the evolution of changes," in Proc. Int'l Conf. on Softw. Maintenance, 2004, pp. 40--49.

Digital Library

[5]

T. Zimmermann, P. Weisgerber, S. Diehl, and A. Zeller, "Mining version histories to guide software changes," in Proc. Int'l Conf. on Softw. Eng., 2004, pp. 563--572.

Digital Library

[6]

A. Ying, G. Murphy, R. Ng, and M. Chu-Carroll, "Predicting source code changes by mining change history," IEEE Trans. Softw. Eng., vol. 30, no. 9, pp. 574--586, 2004.

Digital Library

[7]

R. Robbes, D. Pollet, and M. Lanza, "Logical coupling based on fine-grained change information," in Proc. Working Conf. on Reverse Eng., 2008, pp. 42--46.

Digital Library

[8]

N. Tsantalis, A. Chatzigeorgiou, and G. Stephanides, "Predicting the probability of change in object-oriented systems," IEEE Trans. Softw. Eng., vol. 31, no. 7, pp. 601--614, 2005.

Digital Library

[9]

Y. Zhou and H. Leung, "Predicting object-oriented software maintainability using multivariate adaptive regression splines," Journal of Systems and Software, vol. 80, no. 8, pp. 1349--1361, 2007.

Digital Library

[10]

H. C. Gall, B. Fluri, and M. Pinzger, "Change analysis with evolizer and changedistiller," IEEE Software, vol. 26, no. 1, pp. 26--33, 2009.

Digital Library

[11]

S. R. Chidamber and C. F. Kemerer, "A metrics suite for object oriented design," IEEE Trans. Softw. Eng., vol. 20, no. 6, pp. 476--493, 1994.

Digital Library

[12]

S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications, 1st ed. Camb. Univ. Press, 1994.

[13]

T. Zimmermann and N. Nagappan, "Predicting defects using network analysis on dependency graphs," in Proc. Int'l Conf. on Softw. Eng., 2008, pp. 531--540.

Digital Library

[14]

T. Nguyen, B. Adams, and A. Hassan, "Studying the impact of dependency network measures on software quality," in Proc. Int'l Conf. on Softw. Maint., 2010, pp. 1--10.

Digital Library

[15]

S. Borgatti, M. Everett, and L. Freeman, UCINET 5.0 Version 1.00, Natick: Analytic Technologies., 1999.

[16]

V. Basili, L. Briand, and W. Melo, "A validation of object-oriented design metrics as quality indicators," IEEE Trans. Softw. Eng., vol. 22, no. 10, pp. 751--761, 1996.

Digital Library

[17]

L. C. Freeman, "Centrality in social networks conceptual clarification," Social Networks, vol. 1, no. 3, pp. 215--239, 1979.

[18]

P. Bonacich, "Power and centrality: A family of measures," The American Journal of Sociology, vol. 92, no. 5, pp. 1170--1182, 1987.

[19]

B. Fluri, M. Würsch, M. Pinzger, and H. C. Gall, "Change Distilling: Tree Differencing for Fine-Grained Source Code Change Extraction," IEEE Trans. on Softw. Eng., vol. 33, no. 11, pp. 725--743, 2007.

Digital Library

[20]

A. Schroeter, T. Zimmermann, and A. Zeller, "Predicting component failures at design time," in Proc. Int'l Symp. on Empirical Softw. Eng., 2006, pp. 18--27.

Digital Library

[21]

T. Zimmermann, R. Premraj, and A. Zeller, "Predicting defects for eclipse," in Proc. Int'l Workshop on Predictor Models in Softw. Eng., 2007, pp. 9--15.

Digital Library

[22]

R. Moser, W. Pedrycz, and G. Succi, "A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction," in Proc. Int'l Conf. on Softw. Eng., 2008, pp. 181--190.

Digital Library

[23]

E. Giger, M. Pinzger, and H. C. Gall, "Comparing fine-grained source code changes and code churn for bug prediction," in Proc. Int'l Workshop on Mining Softw. Repositories, 2011, pp. 83--92.

Digital Library

[24]

S. Dowdy, S. Weardon, and D. Chilko, Statistics for Research, 3rd ed., ser. Probability and Statistics. Hoboken, New Jersey: John Wiley and Sons, 2004.

[25]

M. Pinzger, N. Nagappan, and B. Murphy, "Can developer-module networks predict failures?" in Proc. Symp. on the Found. of Softw. Eng., 2008, pp. 2--12.

Digital Library

[26]

S. Lessmann, B. Baesens, C. M. Swantje, and Pietsch, "Bench-marking classification models for software defect prediction: A proposed framework and novel findings," IEEE Trans. on Softw. Eng., vol. 34, no. 4, pp. 485--496, 2008.

Digital Library

[27]

T. Menzies, J. Greenwald, and A. Frank, "Data mining static code attributes to learn defect predictors," IEEE Trans. on Softw. Eng., vol. 33, no. 1, pp. 2--13, 2007.

[28]

D. Kawrykow and M. P. Robillard, "Non-essential changes in version histories," in Proc. Int'l Conf. on Softw. Eng., 2011, pp. 351--360.

Digital Library

[29]

N. Juristo and S. Vegas, "Using differences among replications of software engineering experiments to gain knowledge," in Proc. Int'l Symp. on Empirical Softw. Eng. and Measurement, 2009, pp. 356--366.

Digital Library

[30]

C. Bird, D. Pattison, R. D'Souza, V. Filkov, and P. Devanbu, "Latent social structure in open source projects," in Proc. Int'l Symp. on Found. of Softw. Eng., 2008, pp. 24--35.

Digital Library

[31]

L. González, G. Barahona, and G. Robles, "Applying social network analysis to the information in cvs repositories," in Proc. Int'l Workshop on Mining Softw. Repositories, 2004, pp. 101--105.

[32]

S.-K. Huang and K. min Liu, "Mining version histories to verify the learning process of legitimate peripheral participants," in Proc. Int'l Workshop on Mining Softw. Repositories, 2005, pp. 1--5.

Digital Library

[33]

M. Ohira, N. Ohsugi, T. Ohoka, and K. Matsumoto, "Accelerating cross-project knowledge collaboration using collaborative filtering and social networks," in Proc. Int'l Workshop on Mining Softw. Repositories, 2005, pp. 1--5.

Digital Library

[34]

N. Ducheneaut, "Socialization in an open source software community: A socio-technical analysis," Comput. Supported Coop. Work, vol. 14, pp. 323--368, 2005.

Digital Library

[35]

C. Bird, A. Gourley, P. Devanbu, A. Swaminathan, and G. Hsu, "Open borders? immigration in open source projects," in Proc. Int'l Workshop on Mining Softw. Repositories, 2007, p. 6.

Digital Library

[36]

D. Rombach, "A controlled experiment on the impact of software structure on maintainability," IEEE Trans. on Softw. Eng., vol. 13, no. 3, pp. 344--354, 1987.

Digital Library

[37]

R. Subramanyam and M. Krishnan, "Empirical analysis of ck metrics for object-oriented design complexity: Implications for software defects," IEEE Trans. Softw. Eng., vol. 29, no. 4, pp. 297--310, April 2003.

Digital Library

[38]

T. Khoshgoftaar and R. Szabo, "Improving code churn predictions during the system test and maintenance phases," in Proc. Int'l Conf. on Softw. Maint., 1994, pp. 58--67.

Digital Library

[39]

A. Mockus and D. Weiss, "Predicting risk of software changes," Bell Labs Technical J., vol. 5, pp. 169--180, 2000.

[40]

S. Kim, J. Whitehead, and Y. Zhang, "Classifying software changes: Clean or buggy?" IEEE Trans. Softw. Eng., vol. 34, no. 2, pp. 181--196, March 2008.

Digital Library

[41]

S. Shirabad, T. Lethbridge, and S. Matwin, "Mining the maintenance history of a legacy software system," in Proc. Int'l Conf. on Softw. Maint., 2003, pp. 95--104.

Digital Library

Cited By

Wang SBansal CNagappan NPhilip AMinku LKhomh FPetrić J(2019)Leveraging Change Intents for Characterizing and Identifying Large-Review-Effort ChangesProceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3345629.3345635(46-55)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1145/3345629.3345635
Macho CMcintosh SPinzger MGonzalez-Barahona JHindle ATan L(2017)Extracting build changes with BuildDiffProceedings of the 14th International Conference on Mining Software Repositories10.1109/MSR.2017.65(368-378)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.1109/MSR.2017.65
Dintzner NDeursen APinzger M(2017)Analysing the Linux kernel feature model changes using FMDiffSoftware and Systems Modeling (SoSyM)10.1007/s10270-015-0472-216:1(55-76)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s10270-015-0472-2
Show More Cited By

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Information & Contributors

Information

Published In

cover image ACM Conferences

MSR '12: Proceedings of the 9th IEEE Working Conference on Mining Software Repositories

June 2012

262 pages

ISBN:9781467317610

General Chair:
Michele Lanza
University of Lugano, Switzerland
,
Program Chairs:
Massimiliano Di Penta
University of Sannio, Italy
,
Tao Xie
North Carolina State University

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SIGSOFT: ACM Special Interest Group on Software Engineering
University of Zurich: University of Zurich
IEEE CS

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IEEE Press

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Published: 02 June 2012

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ICSE '12

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SIGSOFT
University of Zurich

ICSE '12: 34th International Conference on Software Engineering

June 2 - 3, 2012

Zurich, Switzerland

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Cited By

Wang SBansal CNagappan NPhilip AMinku LKhomh FPetrić J(2019)Leveraging Change Intents for Characterizing and Identifying Large-Review-Effort ChangesProceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3345629.3345635(46-55)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1145/3345629.3345635
Macho CMcintosh SPinzger MGonzalez-Barahona JHindle ATan L(2017)Extracting build changes with BuildDiffProceedings of the 14th International Conference on Mining Software Repositories10.1109/MSR.2017.65(368-378)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.1109/MSR.2017.65
Dintzner NDeursen APinzger M(2017)Analysing the Linux kernel feature model changes using FMDiffSoftware and Systems Modeling (SoSyM)10.1007/s10270-015-0472-216:1(55-76)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s10270-015-0472-2
Sharma MKumari MSingh VMitra STrajković LBedi PMcIntosh SRajasree M(2015)Post Release Versions based Code Change Quality MetricsProceedings of the Third International Symposium on Women in Computing and Informatics10.1145/2791405.2791466(235-243)Online publication date: 10-Aug-2015
https://dl.acm.org/doi/10.1145/2791405.2791466
Faragó CHegedüs PVégh ÁFerenc R(2014)Connection between version control operations and quality change of the source codeActa Cybernetica10.14232/actacyb.21.4.2014.421:4(585-607)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.14232/actacyb.21.4.2014.4
Nguyen HNguyen ANguyen TNguyen TRajan HDenney EBultan TZeller A(2013)A study of repetitiveness of code changes in software evolutionProceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE.2013.6693078(180-190)Online publication date: 11-Nov-2013
https://dl.acm.org/doi/10.1109/ASE.2013.6693078

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