Abstract:
The applications of machine learning techniques
have shown remarkable improvements for the prediction
of software reliability than traditional statistical
techniques. In this paper, we apply some well-known
machine learning methods such as artificial neural networks,
support vector machines, cascade correlation neural
network, decision trees and fuzzy inference system to
predict the reliability of a software product. The proposed
models have been evaluated using mean absolute error,
root mean squared error, correlation coefficient and precision.
The 16 software life cycle databases have been used
for empirical studies. These databases are extracted from
data and analysis center for software. A comparative
analysis is performed in order to determine the importance
of each method to assess the capability of software reliability
prediction models. We also observe that these
models may use in reliability predictions and results may
be more close to the reality and precision is very effective
with varied real-life failure datasets. Finally we conclude
that proposed approach is more precise in its prediction
capacity having better capability of generalization.