AN EFFICIENT MACHINE LEARNING TOOL FOR AUTOMATIC BUG TRIAGING

Abstract

As technology advances at an exponential rate every day, the development and testing teams do their utmost to address problems as soon as they arise in order to meet customer deadlines. Finding the appropriate developer to address a specific bug is typically simple and quick in small organisations, but it can be challenging for large organisations to find the developer who will be able to address the bug quickly, which is one of the main tasks of bug triaging. In this report, we will examine numerous methods for automatically triaging bugs and attempt to identify the optimal method based on a series of research questions that will enable us to understand the statistical analysis of these methods.