Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Effect of test set minimization on fault detection effectiveness
Proceedings of the 17th international conference on Software engineering
Capture-Recapture Sampling for Estimating Software Error Content
IEEE Transactions on Software Engineering
A Practical Method For The Estimation Of Software Reliability Growth In The Early Stage Of Testing
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
Using Simulation for Assessing the Real Impact of Test Coverage on Defect Coverage
ISSRE '99 Proceedings of the 10th International Symposium on Software Reliability Engineering
A New Challenge for Applying Time Series Metrics Data to Software Quality Estimation
Software Quality Control
| Hi-index | 0.01 |
Regression methods are used to model residual fault density in terms of several product and testing process measures. Process measures considered include discovered fault density, test set size and various coverage measures such as block, decision and all-uses coverage. Product measures considered include lines of code as well as block, decision and all-uses counts. The relative importance of these product/process measures for predicting residual fault density is assessed for a specific data set. Only selected testing process measures, in particular discovered fault density and decision coverage, are important predictors in this case while all product measures considered are important. These results are based on consideration of a substantial family of models, specifically, the family of quadratic response surface models with two-way interaction. Model selection is based on "leave one out at a time" cross-validation using the predicted residual sum of squares (PRESS) criterion.