• KAREDLA CHAYADEVI

Software testing process is a crucial part in software development. Generally, the errors made by developers get fixed at a later stage of the software development process. This increases the impact of the defect. To prevent this, defects need to be predicted during the initial days of the software development, which in turn helps in efficient utilization of the testing resources. Defect prediction process involves classification of software modules into defect prone and non-defect prone. This paper aims to reduce the impact of two major issues faced during defect prediction, i.e., data imbalance and high dimensionality of the defect datasets. Software defect prediction improves software quality and testing efficiency by early identification of defects. Classification models using code attributes are constructed and used for prediction. This paper is a study of software defect prediction using Random Forest and Naïve Bayes classifier and their comparison by showing predictive and comprehensible performance. A framework is proposed for software defect prediction and applied on public domain data sets from NASA PROMISE Repository. The predictive accuracy is observed, which supports the view that software metric-based classification is useful. Furthermore, the accuracy is increased up to 85% or more by means of selecting methods and code attributes of data sets. Output shows that the Random Forest classification models are more superior to the other network models.

software defect prediction, random forest, naive bayes, machine learning

"SOFTWARE DEFECT PREDICTION USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.e672-e677, September-2023, Available :http://www.jetir.org/papers/JETIR2309482.pdf

"SOFTWARE DEFECT PREDICTION USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppe672-e677, September-2023, Available at : http://www.jetir.org/papers/JETIR2309482.pdf