research-article

Effect of temporal collaboration network, maintenance activity, and experience on defect exposure

Authors:

Andriy Miranskyy,

Enzo CialiniAuthors Info & Claims

ESEM '14: Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

Article No.: 27, Pages 1 - 8

https://doi.org/10.1145/2652524.2652586

Published: 18 September 2014 Publication History

Abstract

Context: Number of defects fixed in a given month is used as an input for several project management decisions such as release time, maintenance effort estimation and software quality assessment. Past activity of developers and testers may help us understand the future number of reported defects. Goal: To find a simple and easy to implement solution, predicting defect exposure. Method: We propose a temporal collaboration network model that uses the history of collaboration among developers, testers, and other issue originators to estimate the defect exposure for the next month. Results: Our empirical results show that temporal collaboration model could be used to predict the number of exposed defects in the next month with R² values of 0.73. We also show that temporality gives a more realistic picture of collaboration network compared to a static one. Conclusions: We believe that our novel approach may be used to better plan for the upcoming releases, helping managers to make evidence based decisions.

References

[1]

N. E. Fenton and M. Neil, "A Critique of Software Defect Prediction Models," IEEE Trans Softw Eng, vol. 25, no. 5, pp. 675--689, Sep. 1999.

Digital Library

[2]

B. Caglayan, A. B. Bener, and A. Miranskyy, "Emergence of developer teams in the collaboration network," in 2013 6th Int. Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), 2013, pp. 33--40.

[3]

S. Bicer, A. Bener, and B. Caglayan, "Defect Prediction Using Social Network Analysis on Issue Repositories," in Proc. of the 2011 Int. Conference on Software and Systems Process, New York, NY, USA, 2011, pp. 63--71.

Digital Library

[4]

P. Holme and J. Saramäki, Eds., Temporal Networks. New York: Springer, 2013.

[5]

A. Meneely, L. Williams, W. Snipes, and J. Osborne, "Predicting Failures with Developer Networks and Social Network Analysis," in Proc. of the 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2008, pp. 13--23.

Digital Library

[6]

N. Nagappan, B. Murphy, and V. R. Basili, "The influence of organizational structure on software quality," in ACM/IEEE 30th Int. Conference on Software Engineering, 2008. ICSE '08, 2008, pp. 521--530.

Digital Library

[7]

T. Zimmermann and N. Nagappan, "Predicting Defects Using Network Analysis on Dependency Graphs," in Proc. of the 30th International Conference on Software Engineering, 2008, pp. 531--540.

Digital Library

[8]

C. Bird, N. Nagappan, H. Gall, B. Murphy, and P. Devanbu, "Putting It All Together: Using Socio-technical Networks to Predict Failures," in 20th Int. Symp. on Softw Reliability Eng, 2009. ISSRE '09, 2009, pp. 109--119.

Digital Library

[9]

M. Cataldo and K. Ehrlich, "The Impact of Communication Structure on New Product Development Outcomes," in Proc. of the SIGCHI Conference on Human Factors in Computing Systems, 2012, pp. 3081--3090.

Digital Library

[10]

T. Wolf, A. Schroter, D. Damian, and T. Nguyen, "Predicting Build Failures Using Social Network Analysis on Developer Communication," in Proc. of the 31st International Conference on Softw Eng, 2009, pp. 1--11.

Digital Library

[11]

M. Cataldo and J. D. Herbsleb, "Coordination Breakdowns and Their Impact on Development Productivity and Software Failures," IEEE Trans. Softw. Eng., vol. 39, no. 3, pp. 343--360, Mar. 2013.

Digital Library

[12]

N. Fenton, M. Neil, W. Marsh, P. Hearty, D. Marquez, P. Krause, and R. Mishra, "Predicting Software Defects in Varying Development Lifecycles Using Bayesian Nets," Inf Softw Technol, vol. 49, no. 1, pp. 32--43, Jan. 2007.

Digital Library

[13]

S. Brin and L. Page, "The Anatomy of a Large-scale Hypertextual Web Search Engine," Comput Netw ISDN Syst, vol. 30, no. 1--7, pp. 107--117, Apr. 1998.

Digital Library

[14]

T. J. Ostrand, E. J. Weyuker, and R. M. Bell, "Where the Bugs Are," in Proc. of the 2004 ACM SIGSOFT International Symposium on Software Testing and Analysis, 2004, pp. 86--96.

Digital Library

[15]

T. Menzies, Z. Milton, B. Turhan, B. Cukic, Y. Jiang, and A. Bener, "Defect Prediction from Static Code Features: Current Results, Limitations, New Approaches," Autom. Softw. Engg, vol. 17, no. 4, pp. 375--407, Dec. 2010.

Digital Library

[16]

J. F. Hair, B. Black, B. Babin, R. E. Anderson, and R. L. Tatham, Multivariate Data Analysis, 6^th, Prentice Hall, 2005.

[17]

R. K. Yin, Case study research: design and methods, 3rd ed. Sage Publ., 2003.

[18]

B. Caglayan, A. T. Misirli, G. Calikli, A. Bener, T. Aytac, and B. Turhan, "Dione: An Integrated Measurement and Defect Prediction Solution," in Proc. of the ACM SIGSOFT 20th Int. Symposium on the Foundations of Software Engineering, 2012, pp. 20:1--20:2.

Digital Library

[19]

W. Kruskal, "Relative Importance by Averaging Over Orderings," Am. Stat., vol. 41, no. 1, pp. 6--10, Feb. 1987.

Cited By

Li FZou Z(2021)Subgraph matching on temporal graphsInformation Sciences: an International Journal10.1016/j.ins.2021.07.071578:C(539-558)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.ins.2021.07.071
Sepahvand RAkbari RHashemi S(2020)Predicting the bug fixing time using word embedding and deep long short term memoriesIET Software10.1049/iet-sen.2019.026014:3(203-212)Online publication date: Jun-2020
https://doi.org/10.1049/iet-sen.2019.0260
Miranskyy AAl‐zanbouri ZGodwin DBener A(2018)Database enginesJournal of Software: Evolution and Process10.1002/smr.191530:4Online publication date: 17-Apr-2018
https://dl.acm.org/doi/10.1002/smr.1915
Show More Cited By

Index Terms

Effect of temporal collaboration network, maintenance activity, and experience on defect exposure
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. System management
        Quality assurance
2. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Reusability
        Software product lines
    2. Software verification and validation
      1. Process validation

Recommendations

Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
ESEC/FSE '09: Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering

Prediction of software defects works well within projects as long as there is a sufficient amount of data available to train any models. However, this is rarely the case for new software projects and for many companies. So far, only a few have studies ...
Research on Software Defect Prediction Method Based on Model Reuse
ICEITSA '23: Proceedings of the 3rd International Conference on Electronic Information Technology and Smart Agriculture

Software reuse has been the most effective way to improve the quality and efficiency of software development for a long time. Model-based software reuse(MBSR) development method can greatly improve the efficiency and quality of reuse by providing ...
Formational bounds of link prediction in collaboration networks

Link prediction in collaboration networks is often solved by identifying structural properties of existing nodes that are disconnected at one point in time, and that share a link later on. The maximally possible recall rate or upper bound of this ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ESEM '14: Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

September 2014

461 pages

ISBN:9781450327749

DOI:10.1145/2652524

General Chair:
Maurizio Morisio
Politecnico di Torino, Italy
,
Program Chairs:
Tore Dybå
Sintef, Norway
,
Marco Torchiano
Politecnico di Torino, Italy

Copyright © 2014 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 September 2014

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ESEM '14

Sponsor:

SIGSOFT

ESEM '14: 2014 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

September 18 - 19, 2014

Torino, Italy

Acceptance Rates

ESEM '14 Paper Acceptance Rate 23 of 123 submissions, 19%;

Overall Acceptance Rate 130 of 594 submissions, 22%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
178
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li FZou Z(2021)Subgraph matching on temporal graphsInformation Sciences: an International Journal10.1016/j.ins.2021.07.071578:C(539-558)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.ins.2021.07.071
Sepahvand RAkbari RHashemi S(2020)Predicting the bug fixing time using word embedding and deep long short term memoriesIET Software10.1049/iet-sen.2019.026014:3(203-212)Online publication date: Jun-2020
https://doi.org/10.1049/iet-sen.2019.0260
Miranskyy AAl‐zanbouri ZGodwin DBener A(2018)Database enginesJournal of Software: Evolution and Process10.1002/smr.191530:4Online publication date: 17-Apr-2018
https://dl.acm.org/doi/10.1002/smr.1915
Abdellatif TCapretz LHo DBaresi LMenzies TMetzger AZimmermann T(2015)Software analytics to software practiceProceedings of the First International Workshop on BIG Data Software Engineering10.5555/2819289.2819300(30-36)Online publication date: 16-May-2015
https://dl.acm.org/doi/10.5555/2819289.2819300
Abdellatif TCapretz LHo D(2015)Software Analytics to Software PracticeProceedings of the 2015 IEEE/ACM 1st International Workshop on Big Data Software Engineering10.1109/BIGDSE.2015.14(30-36)Online publication date: 23-May-2015
https://dl.acm.org/doi/10.1109/BIGDSE.2015.14

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