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Modeling software evolution defects: a time series approach

by: Uzma Raja and David P. Hale and Joanne E. Hale

In: Journal of Software Maintenance and Evolution: Research and PracticeDepartment of Information Systems, Statistics and Management Science, University of Alabama, AL, U.S.A.: Copyright © 2008 John Wiley & Sons, Ltd. (2008) , p. n/a.

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URL:	http://dx.doi.org/10.1002/smr.398

Abstract

The Department of Information Systems, Statistics and Management Science, prediction of software defects and defect patterns is and will continue to be a critically important software evolution research topic. This study presents a time series analysis of multi-organizational multi-project defects reported during ongoing software evolution efforts. Using data from monthly defect reports for eight open source software projects over five years, this study builds and tests time series models for each sampled project. The resulting model accounts for the ripple effects of defect detection and correction by modeling the autocorrelation of code defect data. The autoregressive integrated moving average model (0,1,1) was found to hold for all sampled projects and thus provide a basis for both descriptive and predictive software defect analysis that is computationally efficient, comprehensible, and easy to apply. The model may be used to evaluate and compare the reliability of candidate software solutions, and to facilitate planning for software evolution budget and time allocation.

Description

Wiley InterScience :: JOURNALS :: Journal of Software Maintenance and Evolution: Research and Practice

BibTeX record

@article{raja08,
  address = {Department of Information Systems, Statistics and Management Science, University of Alabama, AL, U.S.A.},
  author = {Uzma Raja and David P. Hale and Joanne E. Hale},
  interHash = {75762c784e99f0c84109f5cd931beed7},
  intraHash = {1188afadd2e4c25439842a45db2e27b6},
  journal = {Journal of Software Maintenance and Evolution: Research and Practice},
  note = {pre-publication},
  pages = {n/a},
  publisher = {Copyright © 2008 John Wiley & Sons, Ltd.},
  title = {Modeling software evolution defects: a time series approach},
  url = {http://dx.doi.org/10.1002/smr.398},
  year = {2008},
  doi = {10.1002/smr.398},
  abstract = {The Department of Information Systems, Statistics and Management Science, prediction of software
defects and defect patterns is and will continue to be a critically important software evolution research
topic. This study presents a time series analysis of multi-organizational multi-project defects reported
during ongoing software evolution efforts. Using data from monthly defect reports for eight open source
software projects over five years, this study builds and tests time series models for each sampled project.
The resulting model accounts for the ripple effects of defect detection and correction by modeling the
autocorrelation of code defect data. The autoregressive integrated moving average model (0,1,1) was found
to hold for all sampled projects and thus provide a basis for both descriptive and predictive software
defect analysis that is computationally efficient, comprehensible, and easy to apply. The model may be
used to evaluate and compare the reliability of candidate software solutions, and to facilitate planning for
software evolution budget and time allocation.}
}

Endnote record

%0 Journal Article
%A Uzma Raja
%A David P. Hale
%A Joanne E. Hale
%C Department of Information Systems, Statistics and Management Science, University of Alabama, AL, U.S.A.
%D 2008
%I Copyright © 2008 John Wiley & Sons, Ltd.
%J Journal of Software Maintenance and Evolution: Research and Practice
%K 
%P n/a
%T Modeling software evolution defects: a time series approach
%U http://dx.doi.org/10.1002/smr.398
%X The Department of Information Systems, Statistics and Management Science, prediction of software
defects and defect patterns is and will continue to be a critically important software evolution research
topic. This study presents a time series analysis of multi-organizational multi-project defects reported
during ongoing software evolution efforts. Using data from monthly defect reports for eight open source
software projects over five years, this study builds and tests time series models for each sampled project.
The resulting model accounts for the ripple effects of defect detection and correction by modeling the
autocorrelation of code defect data. The autoregressive integrated moving average model (0,1,1) was found
to hold for all sampled projects and thus provide a basis for both descriptive and predictive software
defect analysis that is computationally efficient, comprehensible, and easy to apply. The model may be
used to evaluate and compare the reliability of candidate software solutions, and to facilitate planning for
software evolution budget and time allocation.