Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study
Empirical Software Engineering
Detecting noisy instances with the rule-based classification model
Intelligent Data Analysis
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
Calculation and optimization of thresholds for sets of software metrics
Empirical Software Engineering
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The cost-effectiveness of software reliability control endeavors can be increased if a software quality estimation was available prior to system tests and operations.If all likely fault-prone (fp) modules were identified prior to operations, then enhanced software reliability can be obtained.Boolean Discriminant Functions (BDFs) have been applied in the past as quality classification models.Simplicity and ease in model interpretation, make BDFs an attractive technique for software quality estimation.Software quality classification models based on BDFs, 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" (GBDFs) as a solution for improving the practical and managerial usefulness of classification models based on BDFs.In addition, the use of GBDFs avoids the need to build complex hybrid classification models in order to improve usefulness of models based on BDFs.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 GBDFs.