OPTIMIZED INCIDENT MATCHING AND AUTO-MATED VERIFICATION OF COMPOSITION PAT-TERNS IN LONG TERM COMPOSED SERVICES

With the increase in the need and demand for evolving Web services, the rate at which changes are made to the services has increased. In case of importunate change requests, there arise critical situations where the business analysts are subjected to make changes by themselves without the aid of the developers because of the time and cost factors. However, there are high chances that an analyst makes a bug introducing change and injects incorrect statements into the logic and hence there are fair chances for the changed service to exhibit an undesired behavior. Though the impact of the changes is analyzed and recorded every time a change is made and the bug report is generated, it is often done many months after the initial injection of the bug which is time consuming and ultimately results in the failure to meet the business outcome. This process is repeated even when similar change requests are encountered which is absurd in the current scenario and acts as a challenge to the success of a business which is the motivation behind this study. This study address this challenge by focusing on an efficient prediction system which would analyze the recorded incidents, filter the incidents which match with the current incident and predict the level of risk and accuracy involved in committing the change. The implication is that the system performs automated verification of composition patterns and detection of violations in the business policies if any and aids in change management.