%0 Journal Article %1 raja08 %A Raja, Uzma %A Hale, David P. %A Hale, Joanne E. %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 imported %P n/a %R 10.1002/smr.398 %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.

@article{raja08, 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.}, added-at = {2008-12-02T19:24:35.000+0100}, address = {Department of Information Systems, Statistics and Management Science, University of Alabama, AL, U.S.A.}, author = {Raja, Uzma and Hale, David P. and Hale, Joanne E.}, biburl = {https://www.bibsonomy.org/bibtex/21188afadd2e4c25439842a45db2e27b6/neilernst}, description = {Wiley InterScience :: JOURNALS :: Journal of Software Maintenance and Evolution: Research and Practice}, doi = {10.1002/smr.398}, interhash = {75762c784e99f0c84109f5cd931beed7}, intrahash = {1188afadd2e4c25439842a45db2e27b6}, journal = {Journal of Software Maintenance and Evolution: Research and Practice}, keywords = {imported}, note = {pre-publication}, pages = {n/a}, publisher = {Copyright © 2008 John Wiley & Sons, Ltd.}, timestamp = {2008-12-02T19:24:35.000+0100}, title = {Modeling software evolution defects: a time series approach}, url = {http://dx.doi.org/10.1002/smr.398}, year = 2008 }