Utilizing Computational Intelligence to Assist in Software Release Decision

Abstract

Defect tracking using computational intelligence methods is used to predict software readiness in this study. By comparing predicted number of faults and number of faults discovered in testing, software managers can decide whether the software are ready to be released or not. Our predictive models can predict: (i) the number of faults (defects), (ii) the amount of code changes required to correct a fault and (iii) the amount of time (in minutes) to make the changes in respective object classes using software metrics as independent variables. The use of neural network model with a genetic training strategy is introduced to improve prediction results for estimating software readiness in this study. Our prediction model is divided into three parts: (1) prediction model for Presentation Logic Tier software components (2) prediction model for Business Tier software components and (3) prediction model for Data Access Tier software components. Existing object-oriented metrics and complexity software metrics are used in the Business Tier neural network based prediction model. New sets of metrics have been defined for the Presentation Logic Tier and Data Access Tier. These metrics are validated using two sets of real world application data, one set was collected from a warehouse management system and another set was collected from a corporate information system.