• RAHUL KUMAR DHALL
• TEJAS CHASKAR
• VEDANT DODMANI
• HRISHI SHARMA
• KASHMIRA JAYKAR

This paper proposes a software defect prediction method using Support Vector Machines (SVM). By leveraging SVM's classification capabilities, the study aims to accurately identify potential defects in software systems. The proposed approach incorporates relevant software metrics to train the SVM model, enabling effective defect prediction. Experimental results demonstrate the effectiveness of the SVM-based approach in identifying software defects.

Support Vector Machine , Supervised Learning , Machine Learning , Software Defect , Classification, Feature Extraction

"SOFTWARE DEFECT PREDICTION USING SVM ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.f472-f477, May-2023, Available :http://www.jetir.org/papers/JETIR2305570.pdf

"SOFTWARE DEFECT PREDICTION USING SVM ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppf472-f477, May-2023, Available at : http://www.jetir.org/papers/JETIR2305570.pdf