Abstract-Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, larger irrelevant crosscompany (CC) data usually makes it difficult to build a prediction model with high performance. On the other hand, brute force leveraging of CC data poorly related to withincompany (WC) data may decrease the prediction model performance. To address such issues, this paper introduces Multi-Source TrAdaBoost algorithm, an effective transfer learning approach to perform CCDP. The core idea of our approach is that: 1) employ limited amount of labeled WC data to weaken the impact of irrelevant CC data; 2) import knowledge not from one but from multiple sources to avoid negative transfer. The experimental results indicate that: 1) our proposed approach achieves the best overall performance among all tested CCDP approaches; 2) only 10% labeled WC data is enough to achieve good performance of CCDP by using our proposed approach.
For example, by removing the data which is poorly related to the company, to mitigate the impact of irrelevant data on forecasting REF .
A Multi-Source TrAdaBoost Approach for Cross- Company Defect Prediction
SEKE
Computer Science