Automated Bug Detection And Fixing Using T5-Small  Transformer Model: A Multi-Language Approach

Md Tanvir Ahamed

Automated Bug Detection And Fixing Using T5-Small Transformer Model: A Multi-Language Approach

Author : Md Tanvir Ahamed Journa Name: International Journal of Scientific Research & Engineering Trends Volume: 12 issue: 2 Year: Volume-12-issue-2 Views : 20

Abstract:

Software bugs remain one of the most persistent challenges in software development, consuming 50-75% of developer time and costing the global economy over $2 trillion annually. This paper presents a multi-language approach to automated bug detection and fixing using the T5-Small transformer model. We construct a dataset of 2,600 real bug examples from Defects4J, BugSwarm, QuixBugs, GitBugs, and 500 novel multi-error examples. The T5-Small model (60M parameters) is fine-tuned with optimal hyperparameters. Our evaluation framework employs seven metrics with mathematical formulations. Experimental results demonstrate 68.46% Normalized Exact Match, 93.74% F1 Score, and 99.55% ROUGE-1. The model performs effectively on both Python (70.0%) and Java (65.0%). All artifacts are released open-source.