Call For Paper Volume:4 Issue:10 Oct'2017 |

Association Rule Mining Scheme for Software Failure Analysis

Publication Date : 31/01/2015



Author(s) :

Ms. R. Gowri , Dr. M. Latha , Mr. R. Subramanian.


Volume/Issue :
Volume 2
,
Issue 1
(01 - 2015)



Abstract :

The software execution process is tracked with event logs. The event logs are used to maintain the execution process flow in a textual log file. The log file also manages the error values and their source of classes. The error values are used to analyze the failure of the software. The data mining methods are used to evaluate the quality and software failure rate analysis process. The text logs are processed and data values are extracted from the data values. The data values are mined using the machine learning methods for failure analysis. The service error, service complaints, interaction error and crash errors are maintained under the log files. The events and their reactions are also maintained under the log files. Software termination and execution failures are identified using the log details. The log file parsing process is applied to extract data from the logs. The associations rule mining methods are used to analyze the log files for failure detection process. The system uses the Weighted Association Rule Mining (WARM) scheme to fetch failure rate in the software execution flow. The system improves the failure rate detection accuracy in WARM model.


No. of Downloads :

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Association Rule Mining Scheme for Software Failure Analysis

January 20, 2015