The invention discloses a boosting based cost sensitive software defect prediction method, belonging to the technical field of software engineering application. According to the invention, re-sampling is carried out by means of Bootstrap, the erroneous deletion of valuable attributes can be prevented by using a cost-sensitive subclass selection manner of randomly deleting an attribute during attribute selection, and meanwhile, the selected attribute sub-class facilitates the reduction of prediction error costs, when the weight is updated, a cost-sensitive weight updating mechanism is adopted, large weight is endowed to a data set with high cost, so that the data can be guaranteed to be studied for many times to obtain a reasonable integrated prediction model, and the prediction model is applied to small sample data to accurately predict software defects, and therefore, the technical problems that the prediction effects are not satisfactory due to the shortage of training data under the small sample data and the unequal cost of false positives and false negatives in the prediction process are solved.