01700nas a2200217   4500008004100000245009300041210006900134260000900203300001300212490000600225520098800231653002901219653001801248653002101266653002001287653002101307100001101328700001401339700001201353856011701365       2007                        eng d00aEmpirically validating software metrics for risk prediction based on intelligent methods0 aEmpirically validating software metrics for risk prediction base  c2007  a99 - 1060 v53 aThe software systems which are related to national science and technology projects are very crucial. This kind of systems always involves high technical 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 four intelligent classification techniques most used in data mining fields, including Bayesian belief networks (BBN), nearest neighbor (NN), rough set (RS) 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 (LOC) 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 obtained results we also found that BBN was more effective than the other three methods in risk prediction.10aBayesian belief networks10aDecision tree10aNearest neighbor10aRisk prediction10aSoftware metrics1 aXu, Z.1 aZheng, X.1 aGuo, P.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-70350695759&partnerID=40&md5=f939c6d69c641cbeee0b6e64381ba65a