A Buffer Overflow Prediction Approach Based on Software Metrics and Machine Learning

Algorithm 1

BOVP based on Gini indexes.

input: D=,(X₂,y₂),…,, represents a feature property set,

represents the predicted attribute set.

output: CART model sets

(1) for in X

(2) for in

(3) search min(Gini);

(4) end for

(5) end for

(6) Build Trees TreeModel from Tj and xi;

(7) if H(D) ≤ && the current depth < &&// sample number of D

(8) Divide D to D1 and D2;

(9) DecisionTreeClassifier (D1, , , );

(10) DecisionTreeClassifier (D2, , , );

(11) else if

(12) drop D

(13) else

(14) D → leaf_Node; // convert to leaf node

(15) predictive class = the most number of classes; // average of samples

(16) break;

(17) end else

(18) return TreeModel

(19) Prediction on TMS.