DocumentCode :
688486
Title :
Software defect prediction using supervised learning algorithm and unsupervised learning algorithm
Author :
Chug, Anuradha ; Dhall, Shafali
Author_Institution :
GGSIPU, USICT, New Delhi, India
fYear :
2013
fDate :
26-27 Sept. 2013
Firstpage :
173
Lastpage :
179
Abstract :
Software defect prediction has recently attracted attention of many software quality researchers. One of the major areas in current project management software is to effectively utilize resources to make meaningful impact on time and cost. A pragmatic assessment of metrics is essential in order to comprehend the quality of software and to ensure corrective measures. Software defect prediction methods are majorly used to study the impact areas in software using different techniques which comprises of neural network (NN) techniques, clustering techniques, statistical method and machine learning methods. These techniques of Data mining are applied in building software defect prediction models which improve the software quality. The aim of this paper is to propose various classification and clustering methods with an objective to predict software defect. To predict software defect we analyzed classification and clustering techniques. The performance of three data mining classifier algorithms named J48, Random Forest, and Naive Bayesian Classifier (NBC) are evaluated based on various criteria like ROC, Precision, MAE, RAE etc. Clustering technique is then applied on the data set using k-means, Hierarchical Clustering and Make Density Based Clustering algorithm. Evaluation of results for clustering is based on criteria like Time Taken, Cluster Instance, Number of Iterations, Incorrectly Clustered Instance and Log Likelihood etc. A thorough exploration of ten real time defect datasets of NASA[1] software project, followed by various applications on them finally results in defect prediction.
Keywords :
data mining; pattern clustering; software metrics; software quality; unsupervised learning; J48 classifier; clustering techniques; corrective measures; data mining classifier algorithms; hierarchical clustering algorithm; k-means clustering algorithm; machine learning methods; make density based clustering algorithm; naive Bayesian classifier; neural network techniques; project management software; random forest classifier; software defect prediction methods; software quality researchers; statistical method; supervised learning algorithm; unsupervised learning algorithm; Decision Trees (J48); Hierarchical Clusterer (HC); K-means (KM); Make Density Based Clusterer (MDBC); Naive Bayesian (NB); Random Forest (RF);
fLanguage :
English
Publisher :
iet
Conference_Titel :
Confluence 2013: The Next Generation Information Technology Summit (4th International Conference)
Conference_Location :
Noida
Electronic_ISBN :
978-1-84919-846-2
Type :
conf
DOI :
10.1049/cp.2013.2313
Filename :
6832328
Link To Document :
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