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Empirically Validating Software Metrics for Risk Prediction Based on Intelligent Methods

The software systems which are related to national projects are always very crucial. This kind of systems always involves hi-tech factors and has to spend a large amount of money, so the quality and reliability of the software deserve to be further studied. Hence, we propose to apply three classification techniques most used in data mining fields: Bayesian belief networks (BBN), nearest neighbor (NN) and decision tree (DT), to validate the usefulness of software metrics for risk prediction. Results show that comparing with metrics such as Lines of code (LOQ and Cyclomatic complexity (V(G)) which are traditionally used for risk prediction, Halstead program difficulty (D), Number of executable statements (EXEC) and Halstead program volume (V) are the more effective metrics as risk predictors. By analyzing we also found that BBN was more effective than the other two methods in risk prediction