Using multiple adaptive regression splines to support decision making in code inspections

Inspections have been shown to be an effective means of detecting defects early on in the software development life cycle. However, they are not always successful or beneficial as they are affected by a number of technical and managerial factors. To make inspections successful, one important aspect is to understand what are the factors that affect inspection effectiveness (the rate of detected defects) in a given environment, based on project data. In this paper we collected data from over 230 code inspections and performed a multivariate statistical analysis in order to look at how management factors, such as the effort assigned and the inspection rate, affect inspection effectiveness. Because the functional form of effectiveness models is a priori unknown, we use a novel exploratory analysis technique: multiple adaptive regression splines (MARS). We compare the MARS model with more classical regression models and show how it can help understand the complex trends and interactions in the data, without requiring the analyst to rely on strong assumptions. Results are reported and discussed in light of existing studies.