research-article

Ownership, experience and defects: a fine-grained study of authorship

Authors:

Premkumar DevanbuAuthors Info & Claims

ICSE '11: Proceedings of the 33rd International Conference on Software Engineering

Pages 491 - 500

https://doi.org/10.1145/1985793.1985860

Published: 21 May 2011 Publication History

Abstract

Recent research indicates that "people" factors such as ownership, experience, organizational structure, and geographic distribution have a big impact on software quality. Understanding these factors, and properly deploying people resources can help managers improve quality outcomes. This paper considers the impact of code ownership and developer experience on software quality. In a large project, a file might be entirely owned by a single developer, or worked on by many. Some previous research indicates that more developers working on a file might lead to more defects. Prior research considered this phenomenon at the level of modules or files, and thus does not tease apart and study the effect of contributions of different developers to each module or file. We exploit a modern version control system to examine this issue at a fine-grained level. Using version history, we examine contributions to code fragments that are actually repaired to fix bugs. Are these code fragments "implicated" in bugs the result of contributions from many? or from one? Does experience matter? What type of experience? We find that implicated code is more strongly associated with a single developer's contribution; our findings also indicate that an author's specialized experience in the target file is more important than general experience. Our findings suggest that quality control efforts could be profitably targeted at changes made by single developers with limited prior experience on that file.

References

[1]

Software errors cost u.s. economy $59.5 billion annually, http://www.nist.gov/public_affairs/releases/n02-10.htm.

[2]

Identifying reasons for software changes using historic databases, volume 0, Los Alamitos, CA, USA, August 2002. IEEE Computer Society.

[3]

Succession: Measuring transfer of code and developer productivity, Washington, DC, USA, June 2009. IEEE Computer Society.

[4]

L. Argote. Organizational Learning: Creating, Retaining, and Transferring Knowledge. Kluwer Academic Publishers, Norwell, MA, USA, 1999.

Digital Library

[5]

L. Argote, S. L. Beckman, and D. Epple. The persistence and transfer of learning in industrial settings. Manage. Sci., 36(2):140--154, 1990.

Digital Library

[6]

A. Bachmann and A. Bernstein. Software process data quality and characteristics: a historical view on open and closed source projects. In IWPSE-Evol '09, 2009.

Digital Library

[7]

R. D. Banker, G. B. Davis, and S. A. Slaughter. Software development practices, software complexity, and software maintenance performance: A field study. MANAGEMENT SCIENCE, 44(4):433--450, April 1998.

Digital Library

[8]

K. Beck. Embracing change with extreme programming. IEEE computer, 32(10):70--77, 1999.

Digital Library

[9]

J. Berkus. The 5 types of open source projects, 2007. March 20, 2007. http://www.powerpostgresql.com/5_types.

[10]

C. Bird, A. Bachmann, E. Aune, J. Duffy, A. Bernstein, V. Filkov, and P. Devanbu. Fair and balanced?: bias in bug-fix datasets. In ESEC/FSE '09, pages 121--130, New York, NY, USA, 2009. ACM.

Digital Library

[11]

C. Bird, N. Nagappan, B. Murphy, H. Gall, and P. Devanbu. An analysis of the effect of code ownership on software quality across windows, eclipse, and firefox. Technical report, University of California, Davis, 2010. Submitted to FSE 2010. http://wwwcsif.cs.ucdavis.edu/~bird/.

[12]

C. Bird, P. Rigby, E. Barr, D. Hamilton, D. German, and P. Devanbu. The Promises and Perils of Mining Git. In MSR 2009, 2009.

Digital Library

[13]

W. Boh, S. Slaughter, and J. Espinosa. Learning from experience in software development: A multilevel analysis. Management Science, 53(8):1315--1331, 2007.

Digital Library

[14]

F. Brooks. The mythical man-month. Addison-Wesley, 1995.

Digital Library

[15]

M. Cataldo, P. Wagstrom, J. Herbsleb, and K. Carley. Identification of coordination requirements: implications for the Design of collaboration and awareness tools. Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, pages 353--362, 2006.

Digital Library

[16]

B. Curtis and H. Iscoe. N.(1988). A field study of the software design process for large systems. Communications of the ACM, 31(11):1268--1287.

Digital Library

[17]

B. Curtis, H. Krasner, and N. Iscoe. A field study of the software design process for large systems. communication of the ACM, 31(11):1268--1287, 1988.

Digital Library

[18]

B. Curtis, E. M. Soloway, R. E. Brooks, J. B. Black, K. Ehrlich, and H. R. Ramsey. Software psychology: the need for an interdisciplinary program. pages 150--164, 1987.

Digital Library

[19]

E. Darr, L. Argote, and D. Epple. The acquisition, transfer, and depreciation of knowledge in service organizations: Productivity in franchises. Management Science, 41(11):1750--1762, 1995.

Digital Library

[20]

J. A. Espinosa. Shared mental models and coordination in large-scale, distributed software development. PhD thesis, Pittsburgh, PA, USA, 2002.

Digital Library

[21]

M. Fischer, M. Pinzger, and H. Gall. Populating a release history database from version control and bug tracking systems. In ICSM '03, pages 23+, Washington, DC, USA, 2003. IEEE Computer Society.

Digital Library

[22]

D. Freedman. Ecological inference and the ecological fallacy. International encyclopedia of the social and behavioral sciences, pages 4027--4030, 2004.

[23]

T. Fritz, G. C. Murphy, and E. Hill. Does a programmer's activity indicate knowledge of code? In ESEC-FSE '07, pages 341--350, New York, NY, USA, 2007. ACM.

Digital Library

[24]

T. Girba, A. Kuhn, M. Seeberger, and S. Ducasse. How developers drive software evolution. In Eighth International Workshop on Principles of Software Evolution (IWPSE'05), pages 113--122, Washington, DC, USA, 2005. IEEE.

Digital Library

[25]

T. L. Graves, A. F. Karr, J. S. Marron, and H. Siy. Predicting fault incidence using software change history. IEEE Trans. Softw. Eng., 26(7):653--661, 2000.

Digital Library

[26]

J. D. Herbsleb and A. Mockus. An empirical study of speed and communication in globally distributed software development. IEEE Trans. Softw. Eng., 29(6):481--494, 2003.

Digital Library

[27]

S. Kan. Metrics and models in software quality engineering. Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA, 2002.

Digital Library

[28]

S. Kim, K. Pan, and E. E. J. Whitehead. Memories of bug fixes. In SIGSOFT '06/FSE-14, pages 35--45, New York, NY, USA, 2006. ACM.

Digital Library

[29]

S. Kim, T. Zimmermann, K. Pan, and J. Jr. Automatic identification of bug-introducing changes. In ASE '06, pages 81--90, Washington, DC, USA, 2006. IEEE Computer Society.

Digital Library

[30]

A. Meneely and L. Williams. Secure open source collaboration: an empirical study of linus' law. In CCS '09, pages 453--462, New York, NY, USA, 2009. ACM.

Digital Library

[31]

A. Mockus and D. M. Weiss. Predicting risk of software changes. Bell Labs Technical Journal, 5(2):169--180, 2000.

[32]

A. Mockus and D. M. Weiss. Predicting risk of software changes. Bell Labs Technical Journal, 5(2):169--180, 2000.

[33]

N. Nagappan, B. Murphy, and V. Basili. The influence of organizational structure on software quality: an empirical case study. In ICSE '08, pages 521--530. ACM, 2008.

Digital Library

[34]

M. Pinzger, N. Nagappan, and B. Murphy. Can developer-module networks predict failures? In SIGSOFT '08/FSE-16, pages 2--12, New York, NY, USA, 2008. ACM.

Digital Library

[35]

G. P. Pisano, R. M. J. Bohmer, and A. C. Edmondson. Organizational differences in rates of learning: Evidence from the adoption of minimally invasive cardiac surgery. Manage. Sci., 47(6):752--768, 2001.

Digital Library

[36]

J. Ratzinger, M. Pinzger, and H. Gall. Eq-mine: Predicting short-term defects for software evolution. In M. B. Dwyer and A. Lopes, editors, Fundamental Approaches to Software Engineering, volume 4422 of Lecture Notes in Computer Science, chapter 3, pages 12--26. Springer Berlin Heidelberg, Berlin, Heidelberg, 2007.

Digital Library

[37]

E. Raymond. The cathedral and the bazaar: musings on Linux and open source by an accidental revolutionary. O'Reilly & Associates, Inc. Sebastopol, CA, USA, 2001.

Digital Library

[38]

P. N. Robillard. The role of knowledge in software development. Communications of the ACM, 42(1):87--92, January 1999.

Digital Library

[39]

M. Sacks. On-the-Job Learning in the Software Industry. Corporate Culture and the Acquisition of Knowledge. Quorum Books, 88 Post Road West, Westport, CT 06881, 1994.

Digital Library

[40]

M. A. Schilling, P. Vidal, R. E. Ployhart, and A. Marangoni. Learning by doing something else: Variation, relatedness, and the learning curve. MANAGEMENT SCIENCE, 49(1):39--56, January 2003.

Digital Library

[41]

T. I. Seifert and B. Paech. Exploring the relationship of history characteristics and defect count: an empirical study. In DEFECTS '08, pages 11--15, New York, NY, USA, 2008. ACM.

Digital Library

[42]

R. Shatnawi and W. Li. The effectiveness of software metrics in identifying error-prone classes in post-release software evolution process. J. Syst. Softw., 81(11):1868--1882, 2008.

Digital Library

[43]

J. Śliwerski, T. Zimmermann, and A. Zeller. When do changes induce fixes? In MSR '05, pages 1--5. ACM, 2005.

Digital Library

[44]

D. Čubranić and G. C. Murphy. Hipikat: recommending pertinent software development artifacts. In ICSE '03, pages 408--418, Washington, DC, USA, 2003. IEEE Computer Society.

Digital Library

[45]

D. G. Wastell. Learning dysfunctions in information systems development: overcoming the social defenses with transitional objects. MIS Q., 23(4):581--600, 1999.

Digital Library

[46]

E. Weyuker, T. Ostrand, and R. Bell. Do too many cooks spoil the broth? using the number of developers to enhance defect prediction models. Empirical Software Engineering, 13(5):539--559, October 2008.

Digital Library

[47]

A. Zeller. Why Programs Fail, Second Edition: A Guide to Systematic Debugging. Morgan Kaufmann, 2 edition, June 2009.

Digital Library

[48]

Y. Zhou and H. Leung. Empirical analysis of object-oriented design metrics for predicting high and low severity faults. IEEE Trans. Softw. Eng., 32(10):771--789, 2006.

Digital Library

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Index Terms

Ownership, experience and defects: a fine-grained study of authorship
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Process validation

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cover image ACM Conferences

ICSE '11: Proceedings of the 33rd International Conference on Software Engineering

May 2011

1258 pages

ISBN:9781450304450

DOI:10.1145/1985793

General Chair:
Richard N. Taylor
UC Irvine, USA
,
Program Chairs:
Harald Gall
University of Zurich, Switzerland
,
Nenad Medvidović
University of Southern California, USA

Copyright © 2011 ACM.

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Published: 21 May 2011

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https://dl.acm.org/doi/10.1145/3707456
Perez-Rosero SDyer RFlint SMcIntosh SSrisa-an W(2025)WIA-SZZ: Work item aware SZZEmpirical Software Engineering10.1007/s10664-025-10616-230:3Online publication date: 1-Mar-2025
https://doi.org/10.1007/s10664-025-10616-2
Xie ZWen MWei ZJin HFilkov VRay BZhou M(2024)Unveiling the Characteristics and Impact of Security Patch EvolutionProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695488(1094-1106)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695488
Razzaq ABuckley JLai QYu TBotterweck G(2024)A Systematic Literature Review on the Influence of Enhanced Developer Experience on Developers' Productivity: Factors, Practices, and RecommendationsACM Computing Surveys10.1145/368729957:1(1-46)Online publication date: 7-Oct-2024
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