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Software Quality Classification Model using Virtual Training Data

가상 훈련 데이터를 사용하는 소프트웨어 품질 분류 모델

  • 홍의석 (성신여자대학교 컴퓨터정보학부)
  • Published : 2008.07.28

Abstract

Criticality prediction models to identify most fault-prone modules in the system early in the software development process help in allocation of resources and foster software quality improvement. Many models for identifying fault-prone modules using design complexity metrics have been suggested, but most of them are training models that need training data set. Most organizations cannot use these models because very few organizations have their own training data. This paper builds a prediction model based on a well-known supervised learning model, error backpropagation neural net, using design metrics quantifying SDL system specifications. To solve the problem of other models, this model is trained by generated virtual training data set. Some simulation studies have been performed to investigate feasibility of this model, and the results show that suggested model can be an alternative for the organizations without real training data to predict their software qualities.

Keywords

Classification Model;Software Quality;Virtual Training Data

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