Volume 12, Number 1

Towards Predicting Software Defects with Clustering Techniques


Waheeda Almayyan, Computer Information Department, Collage of Business Studies, PAAET, Kuwait


The purpose of software defect prediction is to improve the quality of a software project by building a predictive model to decide whether a software module is or is not fault prone. In recent years, much research in using machine learning techniques in this topic has been performed. Our aim was to evaluate the performance of clustering techniques with feature selection schemes to address the problem of software defect prediction problem. We analysed the National Aeronautics and Space Administration (NASA) dataset benchmarks using three clustering algorithms: (1) Farthest First, (2) X-Means, and (3) selforganizing map (SOM). In order to evaluate different feature selection algorithms, this article presents a comparative analysis involving software defects prediction based on Bat, Cuckoo, Grey Wolf Optimizer (GWO), and particle swarm optimizer (PSO). The results obtained with the proposed clustering models enabled us to build an efficient predictive model with a satisfactory detection rate and acceptable number of features.


Software defect prediction, Data mining, Machine learning, Clustering, Feature selection.