Open AccessComparing Fault Prediction Models Using Change Request Data for a Telecommunication System
Author(s) -
Park Young Sik,
Yoon ByeongNam,
Lim JaeHak
Publication year - 1999
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.99.0199.0302
Subject(s) - computer science , multicollinearity , software quality , software , reliability (semiconductor) , data mining , reliability engineering , regression analysis , scale (ratio) , software development , machine learning , engineering , power (physics) , physics , quantum mechanics , programming language
Many studies in the software reliability have attempted to develop a model for predicting the faults of a software module because the application of good prediction models provides the optimal resource allocation during the development period. In this paper, we consider the change request data collected from the field test of a large‐scale software system and develop statistical models of the software module that incorporate a functional relation between the faults and some software metrics. To this end, we discuss the general aspect of regression method, the problem of multicollinearity and the measures of model evaluation. We consider four possible regression models including two stepwise regression models and two nonlinear models. Four developed models are evaluated with respect to the predictive quality.