Programs
- Ph. D. in Mechanical Engineering -
- B.Tech. in Artificial Intelligence (AI) and Data Science (Medical Engineering) - Undergraduate
Publication Type : Conference Proceedings
Publisher : Advances in Intelligent Systems and Computing, International Conference on Computational Intelligence, Cyber Security, and Computational Models, ICC3-2013
Source : Advances in Intelligent Systems and Computing, International Conference on Computational Intelligence, Cyber Security, and Computational Models, ICC3-2013, vol. 246. Springer India, Coimbatore; India, pp. 107-118, 2014
Url : https://link.springer.com/chapter/10.1007/978-81-322-1680-3_12
ISBN : 9788132216803
Keywords : Hyper-quadtree, K-means clustering, Software fault prediction
Campus : Amritapuri, Coimbatore
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Verified : No
Year : 2014
Abstract : Software faults are recoverable errors in a program that occur due to the programming errors. Software fault prediction is subject to problems like non-availability of fault data which makes the application of supervised technique difficult. In such cases, unsupervised techniques are helpful. In this paper, a hyper-quadtree-based K-means algorithm has been applied for predicting the faults in the program module. This paper contains two parts. First, the hyper-quadtree is applied on the software fault prediction dataset for the initialization of the K-means clustering algorithm. An input parameter Δ governs the initial number of clusters and cluster centers. Second, the cluster centers and the number of cluster centers obtained from the initialization algorithm are used as the input for the K-means clustering algorithm for predicting the faults in the software modules. The overall error rate of this prediction approach is compared with the other existing algorithms.
Cite this Research Publication : R. Sasidharan and Padmamala Sriram, “Hyper-Quadtree-Based K-Means Algorithm for Software Fault Prediction”, Advances in Intelligent Systems and Computing, International Conference on Computational Intelligence, Cyber Security, and Computational Models, ICC3-2013, vol. 246. Springer India, Coimbatore; India, pp. 107-118, 2014