… strategy for any industry with high software development costs. In this work, … software defect
prediction by using soft computing based machine learning techniques which helps to predict …

… analysis results we make suggestions for machine learning-… In summary, for JiT defect
prediction, the comparisons with Popt … models both in within-project and cross-project prediction. …

… to perform predictive analysis on data using different machine learning classifiers. … machine
learning methods. Fig 5 shows the distribution of primary studies over machine learning tools …

… In this section, we propose a deep learning-based model for software defect prediction. An
overview of the proposed approach is presented next; then the details are presented in the …

… non-defective instances than defective instances in the datasets used for software defect
prediction (… In contrast, conventional machine learning algorithms assume that the numbers of …

… 2.1 Support vector machine Support Vector Machine (SVM)36 is a machine learning method,
which is effective for solving small-sample, non-linear, and high-dimensional. Generally …

… Performance analysis of machine learning techniques on software defect prediction using
… A feature selection based ensemble classification framework for software defect prediction. …

… Performance analysis of machine learning techniques on software defect prediction using
… A feature selection based ensemble classification framework for software defect prediction. …

… Revisiting the impact of classification techniques on the performance of defect prediction
models,” in … Angelis, “Ranking and clustering software cost estimation models through a multiple …

… machine learning techniques to detect the disease from the input features. Five supervised
machine learning approaches have been used … which the methods and materials used for the …