HYBRID NATURE-BASED AND ENSEMBLE MACHINE LEARNING MODEL FOR SOFTWARE BUG FORECASTING

Due to its significance in fixing the defects found in software testing, the maintenance process of software systems has drawn the attention of researchers in software development systems. BRs include information such as the bug's description, status, reporter, assignee, priority, and severity, among other details. Because the quantity of BRs grows exponentially, personally analysing them all to find system problems becomes an arduous and time-consuming ordeal, which is the primary challenge of this approach. Consequently, it is advisable to use an automated solution.