Softwares play an important role in controlling complex systems. Monitoring the proper
functioning of the components of such systems is the principal role of softwares. Often, a …

Support Vector Machines (SVMs) are kernel-based learning methods, which have been
successfully adopted for regression problems. However, their use in reliability applications …

Different software reliability models, such as parameter and non-parameter models, have
been developed in the past four decades to assess software reliability in the software testing …

This study applies a novel neural-network technique, support vector regression (SVR), to
forecast reliability in engine systems. The aim of this study is to examine the feasibility of …

For the last three decades, reliability growth has been studied to predict software reliability in
the testing/debugging phase. Most of the models developed were based on the non …

Software reliability is the probability of failure-free software operation for a specified period
of time in a specified environment. During the last three decades, many software reliability …

Software reliability models assess the reliability by predicting faults for the software.
Reliability is a real world phenomenon with many associated real-time problems. To obtain …

In this paper, ensemble models are developed to accurately forecast software reliability.
Various statistical (multiple linear regression and multivariate adaptive regression splines) …

Software fault detection and correction processes are related although different, and they
should be studied together. A practical approach is to apply software reliability growth …

Software reliability is an important factor for quantitatively characterizing software quality and
estimating the duration of software testing period. Traditional parametric software reliability …