Prediction of Software Reliability Using Connectionist Models
IEEE Transactions on Software Engineering
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Handbook of software reliability engineering
Handbook of software reliability engineering
Software Reliability Engineered Testing
Software Reliability Engineered Testing
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Software defect prediction using fuzzy support vector regression
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
A study on software reliability prediction models using soft computing techniques
International Journal of Information and Communication Technology
Bug prediction using entropy-based measures
International Journal of Knowledge Engineering and Data Mining
| Hi-index | 0.00 |
In this work, we propose to apply support vector regression (SVR) to build software reliability growth model (SRGM). SRGM is an important aspect in software reliability engineering. Software reliability is the probability that a given software will be functioning without failure during a specified period of time in a specified environment. In order to obtain the better performance of SRGM, practical selection of parameter C for SVR is discussed in the experiments. Experimental results with the classical Sys1 and Sys3 SRGM data set show that the performance of the proposed SVR-based SRGM is better than conventional SRGMs and relative good prediction and generalization ability are achieved.