Evaluating the accuracy of defect estimation models based on inspection data from two inspection cycles

Abstract

Defect content estimation techniques (DCETs), based on defect data from inspection, estimate the total number of defects in a document to evaluate the development process. For inspections that yield few data points DCETs reportedly underestimate the number of defects. If there is a second inspection cycle, the additional defect data is expected to increase estimation accuracy. In this paper we consider 3 scenarios to combine data sets from the inspection-reinspection process. We evaluate these approaches with data from an experiment in a university environment where 31 teams inspected and reinspected a software requirements document. Main findings of the experiment were that reinspection data improved estimation accuracy. With the best combination approach all examined estimators yielded on average estimates within 20% around the true value, all estimates stayed within 40% around the true value.