Enhanced Accuracy and Prediction of Novel Random Forest Algorithm Compared over K-Nearest Neighbor in Software Bug Prediction System

• Published in: 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) pp. 1 - 6
• Main Authors: Bharath, KS, Nagalakshmi, T. J., Meenakshisundaram, N.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.04.2024
• Subjects: Bug prediction; Codes; Complexity theory; Computer bugs; Decision Tree (DT); K-Nearest Neighbor (KNN); Machine learning (ML); Novel Random Forest (RF); Prediction algorithms; Radio frequency; Security threats; Software; Software algorithms; Software bugs (SB); Software defects

The purpose of this research is to compare and contrast the effectiveness of the traditional K-Nearest Neighbor (KNN) method with a new approach based on random forests (RF) for detecting software bugs (SB). The main goal is to evaluate how well the proposed technique finds and predicts software pro...