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Software Bug Classification Using Machine Learning

Authors:

Manoj Sutar, Prof. Shubhangi Vitalkar

Manoj Sutar, Department of MCA, Trinity Academy Of Engineering, Pune, India

 Prof. Shubhangi Vitalkar,  Department of MCA, Trinity Academy Of Engineering, Pune, India

Abstract

This research presents a Software Bug Classification system that utilizes machine learning and natural language processing to automatically categorize bug reports. The system is trained on labeled datasets containing software issue descriptions and applies text classification techniques to identify the type of bug—such as functionality, performance, or user interface-related issues. Machine learning models like Support Vector Machines (SVM), Naive Bayes, and transformer-based models such as BERT are used to enhance classification accuracy. A user-friendly web interface developed using Flask enables users to submit bug reports and receive real-time predictions. The system improves the efficiency of the bug triage process and supports faster software maintenance by reducing manual effort.

Keywords

Bug Classification, Machine Learning, Natural Language Processing, Text Classification, BERT, Flask, Software Maintenance, Automation, Software Engineering, Bug Triage