Stability of Software Defect Prediction in Relation to Levels of Data Imbalance (CROSBI ID 603311)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Galinac Grbac, Tihana ; Mauša, Goran ; Dalbelo Bašić, Bojana
engleski
Stability of Software Defect Prediction in Relation to Levels of Data Imbalance
Software defect prediction is an important decision support activity in software quality assurance. Its goal is reducing verification costs by predicting the system modules that are more likely to contain defects, thus enabling more efficient allocation of resources in verification process. The problem is that there is no widely applicable well performing prediction method. The main reason is in the very nature of software datasets, their imbalance, complexity and properties dependent on the application domain. In this paper we suggest a research strategy for the study of the performance stability using different machine learning methods over different levels of imbalance for software defect prediction datasets. We also provide a preliminary case study on a dataset from the NASA MDP open repository using multivariate binary logistic regression and forward and backward feature selection. Results indicate that the performance becomes unstable around 80% of imbalance.
Software Defect Prediction; Data Imbalance; Feature Selection; Stability
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Podaci o prilogu
1-10.
2013.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of SQAMIA 2013
Budimac, Zoran
Novi Sad:
978-86-7031-269-2
Podaci o skupu
Second Workshop on Software Quality Analysis, Monitoring, Improvement and Applications
predavanje
15.09.2013-17.09.2013
Novi Sad, Srbija