Software defect prediction aims at the very early step of software quality control, helps software engineers focus their attention on defect-prone parts during verification process. Cross-project defect predictions are proposed in which prediction models are trained by using sufficient training data from already existed software projects and predict defect in some other projects, however, their performances are always poor. The main reason is that, the divergence of the data distribution among different software projects causes a dramatic impact on the prediction accuracy. This study proposed an approach of cross-project defect prediction by applying a supervised domain adaptation based on instance weighting. The sufficient instances drawn from some source project are weighted by assigning target-dependent weights to the loss function of the prediction model when minimizing the expected loss over the distribution of source data, so that the distribution properties of the data from target project can be matched to the source project. Experiments including dataset selection, data preprocessing and results are described over different experiment strategies on ten open-source software projects. Over fitting problems are also studied through different levels including dataset, prediction model and domain adaptation process. The results show that the proposed approach is close to the performance of within-project defect prediction, better than similar approach and significantly better that of the baseline.