Improving usefulness of software quality classification models based on Boolean discriminant functions

The use of generalized Boolean discriminant functions (GBDF) is proposed as a solution for improving the practical and managerial usefulness of classification models based on BDF, and the use of GBDF avoids the need to build complex hybrid classification models in order to improve usefulness of models based in BDF.

Abstract

BDF (Boolean discriminant functions) are an attractive technique for software quality estimation. Software quality classification models based on BDF provide stringent rules for classifying not fault-prone modules (nfp), thereby predicting a large number of modules as fp. Such models are practically not useful from software quality assurance and software management points of view. This is because, given the large number of modules predicted as fp, project management will face a difficult task of deploying, cost-effectively, the always-limited reliability improvement resources to all the fp modules. This paper proposes the use of generalized Boolean discriminant functions (GBDF) as a solution for improving the practical and managerial usefulness of classification models based on BDF. In addition, the use of GBDF avoids the need to build complex hybrid classification models in order to improve usefulness of models based on BDF. A case study of a full-scale industrial software system is presented to illustrate the promising results obtained from using the proposed classification technique using GBDF.