Investigating Defect Prediction Models for Iterative Software Development When Phase Data is Not Recorded Lessons Learned

摘要

One of the biggest problems that software organizations encounter is specifying the resources required and the duration of projects. Organizations that record the number of defects and the effort spent on fixing these defects are able to correctly predict the latent defects in the product and the effort required to remove these latent defects. The use of reliability models reported in the literature is typical to achieve this prediction, but the number of studies that report defect prediction models for iterative software development is scarce. In this article we present a case study which predicts the defectiveness of new releases in an iterative, civil project where defect arrival phase data is not recorded. We investigated Linear Regression Model and Rayleigh Model one of the statistical reliability model that contain time information, to predict the module level and project level defectiveness of the new releases of an iterative project through the iterations. The models were created by using 29 successive releases for the project level and 15 successive releases for the module level defect density data. This article explains the procedures that were applied to generate the defectiveness models and the lessons learned from the studies.