Abstract: A set of predicted binary quality indexes is created from a sample set of application lifecycle information and customer encountered defects (CED) for each module id and revision (rev) pair for each application. Normalized effort and quality related factors are extracted for each module id and rev pair of each application. A binary quality index is created based on a set of weighted CED ratings for each module id and rev pair of each application. A prediction model for the binary quality index is created by training a decision tree-based classifier with the sample set to create a set of prediction weights for each effort and quality factor. The set of prediction weights is applied to the effort and quality related factors to each module id and rev pair of an application under-development to create the set of predicted binary quality indexes.

Abstract: A set of predicted binary quality indexes is created from a sample set of application lifecycle information and customer encountered defects (CED) for each module id and revision (rev) pair for each application. Normalized effort and quality related factors are extracted for each module id and rev pair of each application. A binary quality index is created based on a set of weighted CED ratings for each module id and rev pair of each application. A prediction model for the binary quality index is created by training a decision tree-based classifier with the sample set to create a set of prediction weights for each effort and quality factor. The set of prediction weights is applied to the effort and quality related factors to each module id and rev pair of an application under-development to create the set of predicted binary quality indexes.