PREDICTION OF DEFECT DENSITY FOR OPEN SOURCE SOFTWARE USING REPOSITORY METRICS
Open source software refers to software with unrestricted access for use or modification. Many software development organizations are using this open source methodology in their development process. Many software developers can work in parallel with the open source project using the web as a shared resource. The defect density of such projects is often required to be predicted for the purpose to ensure quality standards. Static metrics for defect density prediction require extraction of abstract information from the code. Repository metrics, on the other hand, are easy to extract from the repository data sets. In this paper, an analysis has been performed over repository metrics of open source software. Further, defect density is being predicted using these metrics individually and jointly. Sixty two open source software are considered for analysis using Simple and Multiple Linear Regression methods as statistical procedures. The results reveal a statistically significant level of acceptance for prediction of defect density using few repository metrics individually and jointly.
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