CONSTRUCTION OF IMPROVED PROCESS MODELS BY CLUSTERING EVENT LOGS

One main motive is to solve the problem that presently exist in process discovery, which includes unable to search out correct and understandable process models out of event logs stemming from exceptionally flexible environments. Programming analyst spend extra time in dealing with programming bugs. An unavoidable stride of fixing bugs is bug processing in a system, because of this to efficaciously relegate a designer to every other bug. To decrease the time cost in manual work, content classifications are linked to lead programmed bug processing. This system addresses the difficulty of statistics diminishment for bug processing in a process improvement, i.e., the way to lessen the scale and identify the character of bug facts. To conquer those problems proposed system provides an automatic way for software engineers to generate mined process from systematic event logs specification and bug reviews consist of problem fixing, operating to gain others and technical task. This system focuses on characteristics from chronicled bug information units and fabricates a prescient model for each alternative bug statistics set.