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An initial study of the growth of eclipse defects

Published: 10 May 2008 Publication History

Abstract

We analyze the Eclipse defect data from June 2004 to November 2007, and find that the growth of the number of defects can be well modeled by polynomial functions. Furthermore, we can predict the number of future Eclipse defects based on the nature of defect growth.

References

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C. Andersson and P. Runeson, A Replicated Quantitative Analysis of Fault Distributions in Complex Software Systems, IEEE Trans. Software Eng., 33 (5), pp. 273--286, May 2007.
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L.C. Briand and I. Wieczorek, Resource estimation in software engineering, Encyclopedia of Software Engineering, (ed. J. Marcinak), Wiley, 2002. 1160--1196.
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J. Cohen, Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates, 2003.
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Eclipse bug dataset, available at the MSR 2008 Mining Challenge website http://msr.uwaterloo.ca/msr2008/.
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N. Fenton and N. Ohlsson, Quantitative Analysis of Faults and Failures in a Complex Software System, IEEE Trans. Software Eng., 26 (8), Aug 2000. pp. 797--814.
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H. Joshi, C. Zhang, S. Ramaswamy, C. Bayrak, Local and Global Recency Weighting Approach to Bug Prediction, Proc. 4th Int'l workshop on Mining Software Repositories (MSR 2007), Minneapolis, USA, 2007.
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T. Menzies, J. Greenwald and A. Frank, Data Mining Static Code Attributes to Learn Defect Predictors, IEEE Trans. Software Eng., vol. 32, no. 11, 2007.
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Rao, C. R., Prediction of Future Observations in Growth Curve Models, Statistical Science, vol. 2(4), 434--447, 1987.
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H. Zhang and X. Zhang, Comments on "Data Mining Static Code Attributes to Learn Defect Predictors", IEEE Trans. on Software Eng., vol. 33(9), Sep 2007.
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H. Zhang, X. Zhang, M. Gu, Predicting Defective Software Components from Code Complexity Measures, Proc. 13th IEEE Pacific Rim Int'l Symp. on Dependable Computing Conference (PRDC 2007), Melbourne, Australia, Dec 2007, IEEE Press, pp. 93--96.
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H. Zhang, On the Distribution of Software Faults, IEEE Trans. on Software Eng., vol. 34(2), 2008.

Cited By

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  • (2018)SamEn‐SVR: using sample entropy and support vector regression for bug number predictionIET Software10.1049/iet-sen.2017.016812:3(183-189)Online publication date: Jun-2018
  • (2017)Temporal Modelling of Bug Numbers of Open Source Software Applications Using LSTMIntelligent Systems Technologies and Applications10.1007/978-3-319-68385-0_16(189-203)Online publication date: 21-Oct-2017
  • (2014)A comparison of ARIMA, neural network and a hybrid technique for Debian bug number prediction2014 International Conference on Computer and Communication Technology (ICCCT)10.1109/ICCCT.2014.7001468(47-53)Online publication date: Sep-2014
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Published In

cover image ACM Conferences
MSR '08: Proceedings of the 2008 international working conference on Mining software repositories
May 2008
162 pages
ISBN:9781605580241
DOI:10.1145/1370750
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]

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Publication History

Published: 10 May 2008

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Author Tags

  1. defect growth model
  2. defect prediction
  3. eclipse
  4. polynomial regression

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Cited By

View all
  • (2018)SamEn‐SVR: using sample entropy and support vector regression for bug number predictionIET Software10.1049/iet-sen.2017.016812:3(183-189)Online publication date: Jun-2018
  • (2017)Temporal Modelling of Bug Numbers of Open Source Software Applications Using LSTMIntelligent Systems Technologies and Applications10.1007/978-3-319-68385-0_16(189-203)Online publication date: 21-Oct-2017
  • (2014)A comparison of ARIMA, neural network and a hybrid technique for Debian bug number prediction2014 International Conference on Computer and Communication Technology (ICCCT)10.1109/ICCCT.2014.7001468(47-53)Online publication date: Sep-2014
  • (2013)Time series prediction of debian bug data using autoregressive neural network2013 4th International Conference on Computer and Communication Technology (ICCCT)10.1109/ICCCT.2013.6749612(110-115)Online publication date: Sep-2013
  • (2012)An Empirical Analysis of Software Changes on Statement Entity in Java Open Source ProjectsInternational Journal of Open Source Software and Processes10.4018/jossp.20120401024:2(16-31)Online publication date: 1-Apr-2012
  • (2012)Predicting defect numbers based on defect state transition modelsProceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement10.1145/2372251.2372287(191-200)Online publication date: 19-Sep-2012
  • (2011)Are change metrics good predictors for an evolving software product line?Proceedings of the 7th International Conference on Predictive Models in Software Engineering10.1145/2020390.2020397(1-10)Online publication date: 20-Sep-2011
  • (2010)Test generation via Dynamic Symbolic Execution for mutation testingProceedings of the 2010 IEEE International Conference on Software Maintenance10.1109/ICSM.2010.5609672(1-10)Online publication date: 12-Sep-2010
  • (2009)Using Dependency Information to Select the Test Focus in the Integration Testing ProcessProceedings of the 2009 Testing: Academic and Industrial Conference - Practice and Research Techniques10.1109/TAICPART.2009.14(135-143)Online publication date: 4-Sep-2009
  • (2009)Evaluating Process Quality Based on Change Request Data --- An Empirical Study of the Eclipse ProjectProceedings of the International Conferences on Software Process and Product Measurement10.1007/978-3-642-05415-0_17(227-241)Online publication date: 9-Nov-2009

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