Handbook of software reliability engineering
Handbook of software reliability engineering
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Support vector fuzzy regression machines
Fuzzy Sets and Systems - Theme: Learning and modeling
An Empirical Study on Testing and Fault Tolerance for Software Reliability Engineering
ISSRE '03 Proceedings of the 14th International Symposium on Software Reliability Engineering
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
A Novel Method for Early Software Quality Prediction Based on Support Vector Machine
ISSRE '05 Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering
Predicting Defects for Eclipse
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Automating algorithms for the identification of fault-prone files
Proceedings of the 2007 international symposium on Software testing and analysis
IEEE Transactions on Software Engineering
Regression via Classification applied on software defect estimation
Expert Systems with Applications: An International Journal
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Support vector regression for software reliability growth modeling and prediction
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Support vector machines for regression and applications to software quality prediction
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
IEEE Transactions on Neural Networks
A genetic algorithm to configure support vector machines for predicting fault-prone components
PROFES'11 Proceedings of the 12th international conference on Product-focused software process improvement
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Regression techniques have been applied to improve software quality by using software metrics to predict defect numbers in software modules This can help developers allocate limited developing resources to modules containing more defects In this paper, we propose a novel method of using Fuzzy Support Vector Regression (FSVR) in predicting software defect numbers Fuzzification input of regressor can handle unbalanced software metrics dataset Compared with the approach of support vector regression, the experiment results with the MIS and RSDIMU datasets indicate that FSVR can get lower mean squared error and higher accuracy of total number of defects for modules containing large number of defects.