Construction of error prone module reusing prediction model

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In error prone module prediction, we construct a predictor using some prediction algorithm and some training data, then we predict the error proneness of testing data by using this predictor, so construction of adequate predictor for each testing data is an important problem. At first we tried data adjustment based on the similarity between the training data and the testing data, but we found that other project specific characteristics affect the accuracy of prediction.In the next place, we considered knowledge based predictor mining method. In this method we construct the knowledge base which consists of many project data and their prediction accuracy data using several prediction algorithms and the project specific data. We constructed test data set and by using these data set and public data set, we showed the effectiveness of this method.We showed only the possibility of this method, so further consideration and prototype prediction tool construction are needed.