An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method

The quality of software measurement data affects the accuracy of project manager's decision making using estimation or prediction models and the understanding of real project status. During the software measurement implementation, the outlier which reduces the data quality is collected, however its detection is not easy. To cope with this problem, we propose an approach to outlier detection of software measurement data using the k-means clustering method in this work.