Analysis of Software Bug Localization Models from A Statistical Perspective using Machine Learning

In the ever-evolving digital world, software dependability is a must. With a commitment to software quality assurance excellence, the Bug Localization Project hopes to strengthen and certify the digital ecosystem. The Bug Localization Project is a key component in the continuous attempt to achieve higher software quality in the dynamic field of software development. The urgent need for effective bug localization and identification is what this project is meant to solve. Our goal is to accurately identify, classify, and record shortcomings in software using state-of-the-art techniques and tools. This will enable much quicker problem solving, lower development costs, and ultimately higher user happiness. The primary objective of this program is to expedite the process of localizing bugs. Our goal is to reduce software bug-related disruptions and downtime by improving the accuracy and efficiency of issue detection and reporting. Our group works closely alongside one another all the time to eliminate software flaws. We propose a seamless experience for software users through careful bug localization. Specifically, to increase the accuracy of the insect localization job, we suggested a machine learning model that incorporates methods such as CNN, TF-IDF, Bayesian belief network, deep learning, information retrieval techniques, and Abstract Syntax Trees (ASTs) that can be trained on datasets.