Using Time Series Models for Defect Prediction in Software Release Planning
Document Type
Oral Presentation
Campus where you would like to present
SURC 137B
Start Date
21-5-2015
End Date
21-5-2015
Keywords
Software Defect Prediction, Release Planning, Time Series Model
Abstract
To produce a high-quality software release, sufficient time should be allowed for testing and fixing defects. Otherwise, there is a risk of slip in the development schedule and/or software quality. A time series model is used to predict the number of bugs created during development. The model depends on the previous numbers of bugs created. The model also depends, in an exogenous manner, on the previous numbers of new features resolved and improvements resolved. This model structure would allow hypothetical release plans to be compared by assessing their predicted impact on testing and defect-fixing time. The VARX time series model was selected as a reasonable approach. The accuracy of the model appeared low for a single dataset, but the error was found to be normally distributed.
Recommended Citation
Tunnell, James, "Using Time Series Models for Defect Prediction in Software Release Planning" (2015). Symposium Of University Research and Creative Expression (SOURCE). 5.
https://digitalcommons.cwu.edu/source/2015/oralpresentations/5
Department/Program
Computer Science
Additional Mentoring Department
Computer Science
Using Time Series Models for Defect Prediction in Software Release Planning
SURC 137B
To produce a high-quality software release, sufficient time should be allowed for testing and fixing defects. Otherwise, there is a risk of slip in the development schedule and/or software quality. A time series model is used to predict the number of bugs created during development. The model depends on the previous numbers of bugs created. The model also depends, in an exogenous manner, on the previous numbers of new features resolved and improvements resolved. This model structure would allow hypothetical release plans to be compared by assessing their predicted impact on testing and defect-fixing time. The VARX time series model was selected as a reasonable approach. The accuracy of the model appeared low for a single dataset, but the error was found to be normally distributed.
Faculty Mentor(s)
John Anvik