Unsupervised Learning Model for Fault Prediction Using Representative Clustering Algorithms


KIPS Transactions on Software and Data Engineering, Vol. 3, No. 2, pp. 57-64, Feb. 2014
10.3745/KTSDE.2014.3.2.57,   PDF Download:

Abstract

Most previous studies of software fault prediction model which determines the fault-proneness of input modules have focused on supervised learning model using training data set. However, Unsupervised learning model is needed in case supervised learning model cannot be applied: either past training data set is not present or even though there exists data set, current project type is changed. Building an unsupervised learning model is extremely difficult that is why only a few studies exist. In this paper, we build unsupervised models using representative clustering algorithms, EM and DBSCAN, that have not been used in prior studies and compare these models with the previous model using K-means algorithm. The results of our study show that the EM model performs slightly better than the K-means model in terms of error rate and these two models significantly outperform the DBSCAN model.


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Cite this article
[IEEE Style]
E. S. Hong and M. K. Park, "Unsupervised Learning Model for Fault Prediction Using Representative Clustering Algorithms," KIPS Transactions on Software and Data Engineering, vol. 3, no. 2, pp. 57-64, 2014. DOI: 10.3745/KTSDE.2014.3.2.57.

[ACM Style]
Euy Seok Hong and Mi Kyeong Park. 2014. Unsupervised Learning Model for Fault Prediction Using Representative Clustering Algorithms. KIPS Transactions on Software and Data Engineering, 3, 2, (2014), 57-64. DOI: 10.3745/KTSDE.2014.3.2.57.