Guest editorial: learning to organize testing

Abstract

At the start of the decade, two publications (Shull et al. 2002; Boehm and Basili 2001) described the start-of-the art in defect reduction. Since then, there has been considerable research into data mining of defect data; e.g. Menzies et al. (2007). The data mining work has become less about defect reduction, and more about how to organize a project’s test resources in order to improve product quality by (say) defining a procedure such that the modules most likely to contain defects are inspected first (Menzies et al. 2010). After a decade of intensive work into data mining to make best use of testing resources, it is time to ask: what have we learned from all that research? Some of that research offers success stories with (e.g.) • Reducing the costs to find defects (Menzies et al. 2010); • Generalizing defect predictors to other projects (Tosun et al. 2011); • Tuning those predictors to different business goals (Turhan et al. 2009).