Towards Optimized K Means Clustering using Nature-inspired Algorithms for Software Bug Prediction

Authors

  • Tameswar Kajal

  • Geerish Suddul

  • Kumar Dookhitram

Keywords:

data clustering, K-means algorithm, Nature-inspired algorithms, software bug detection, coral reefs

Abstract

In today s software development environment the necessity for providing quality software products has undoubtedly remained the largest difficulty As a result early software bug prediction in the development phase is critical for lowering maintenance costs and improving overall software performance Clustering is a well-known unsupervised method for data classification and finding related patterns hidden in datasets

How to Cite

Tameswar Kajal, Geerish Suddul, & Kumar Dookhitram. (2023). Towards Optimized K Means Clustering using Nature-inspired Algorithms for Software Bug Prediction. Global Journal of Computer Science and Technology, 23(C1), 35–44. Retrieved from https://computerresearch.org/index.php/computer/article/view/102298

Towards Optimized K Means Clustering using Nature-inspired Algorithms for Software Bug Prediction

Published

2023-05-20