• Varshini Balanagu
• Tanuku Sai Lalitha Jyothi
• Dr. Bh. V. S. R. K. Raju
• Vadlamuru Rithika Naga Anusha
• Rudraraju P D Chandini

The entire success of software is impacted by software bug prediction (SBP), a crucial part of the software development and maintenance life cycles. It is necessary to anticipate issues in order to increase the software's dependability, efficiency, and cost. In spite of the fact that a number of methods have been put out in the literature, it is difficult to create a reliable bug prediction model. In this paper, a machine learning (ML) based prediction method for software issues is introduced. Algorithms for guided machine learning that are based on historical data have been used to predict software errors. Two of the classifiers are Support Vector Machine and Naive Bayes (NB). How accurate and useful ML methods can be employed was demonstrated through the evaluation process. Included is the utilization of a comparative measure.

Naïve Bayes; Convolutional Neural Network; Decision Tree; Regression; Support Vector Machine; Fault Prediction.

"Software Bug Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.k122-k130, June-2023, Available :http://www.jetir.org/papers/JETIR2306A14.pdf

"Software Bug Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppk122-k130, June-2023, Available at : http://www.jetir.org/papers/JETIR2306A14.pdf