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http://dx.doi.org/10.3745/KIPSTD.2003.10D.4.689

A Software Quality Prediction Model Without Training Data Set  

Hong, Euy-Seok (성신여자대학교 컴퓨터정보학부)
Publication Information
The KIPS Transactions:PartD / v.10D, no.4, 2003 , pp. 689-696 More about this Journal
Abstract
Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone are used for identifying trouble spots of software system in analysis or design phases. Many criticality prediction models for identifying fault-prone modules using complexity metrics have been suggested. But most of them need training data set. Unfortunately very few organizations have their own training data. To solve this problem, this paper builds a new prediction model, KSM, based on Kohonen SOM neural networks. KSM is implemented and compared with a well-known prediction model, BackPropagation neural network Model (BPM), considering internal characteristics, utilization cost and accuracy of prediction. As a result, this paper shows that KSM has comparative performance with BPM.
Keywords
Criticality; Prediction Model; KSM;
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Times Cited By KSCI : 2  (Citation Analysis)
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