| dc.description.abstract | Nowadays, cloud computing systems, software defined networks, content
delivery networks, which contain numerous servers and network devices, are growing
increasingly. Therefore, there is a big challenging to manage the scalability,
availability, heterogeneity and importance of those system, whereas the human ability
and assistant software are restricted. There is continuously a request of creating new
methods and tools that support administrators in monitoring errors.
Although, analysis event monitoring and fault detection has been considered
by several approaches. However, this thesis points to study and apply Random Forest
technique to analyze fault dataset. In fact, most of the existing approach usually points
to detect and solve error on log event, message and trace by administrator's expertise.
In fact, these methods intensely depend on human being. So, applying analytic
techniques to determine significant facts of a problem, which helps administrator deal
with the error and then reducing the dependency of human being.
In computer science, there are various analytic techniques for classification
and regression such as Decision Tree, K-means and Random Forest, which is one of
them. Although, this technique is younger than Neural Network, Decision Tree, Kmeans, or Bayesian and existing some weakness; however, it has various strong
features in classification and regression. In this study, Random Forest technique
applied to estimate fault particularly in bug report. Last but not least, this proposal is
contributing apart from the content of the publishing research paper namely “Analysis
of Software Bug Reports Using Random Forest [1]” on ACIIDS 2018 conference | en_US |
