AUTOMATED SOFTWARE BUG DETECTION USING MACHINE LEARNING
CHINTHA.DURGA RAM CHARAN TEJ · International Journal of Novel Research and Development · 2025
SOFTWARE BUGS CAN LEAD TO SECURITY VULNERABILITES,SYSTEM FAILURES, AND FINANCIAL LOSSES. TRADITIONAL BUG DETECTION METHODS RELY ON MANUAL CODE REVIEWS AND STATIC ANALYSIS,WHICH ARE TIME -CONSUMING AND ERROR-PRONE. THIS IS REASERCH PROPOSE AS AUTOMATED BUG DETECTION SYSTEM USING MACHINE LEARNING(ML) TO IMPROVE SOFTWARE QUALITY ASSURANCE.OUR MODEL LEVERAGES NATURAL LANGUAGE PROCESSING(NLP) AND DEEP LEARNING TO ANALYSE SOURCE CODE AND DETECT POTENTIAL BUGS WITH HIGH ACCURACY. WE COMPARE DIFFERENT ML ALGORITHMS,SUCH AS RANDOM FOREST,SUPPORT VECTOR MACHINE(SVM),AND DEEP NEURAL NETWORKS,TO DETERMINE THE MOST EFFECTIVE APPROACH. THE PROPOSED SYSTEM OUT PERFORMS TRADITIONAL METHODS IN TERMS OF PRECISION,RECALL, AND F1-SCORE,MAKING IT A PROMISING SOLUTION FORAUTOMATED SOFTWARE DEBUGGING.