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Developers often write low-quality code comments due to the lack of programming experience, which can reduce the efficiency of developers program comprehension. Therefore, developers hope that code comment generation tools can be developed to illustrate the functionality and purpose of the code. Recently, researchers mainly model this problem as the neural machine translation problem and tend to use deep learning-based methods. In this study, we propose a novel method ComFormer based on Transformer and fusion method-based hybrid code presentation. Moreover, to alleviate OOV (out-of-vocabulary) problem and speed up model training, we further utilize the Byte-BPE algorithm to split identifiers and Sim_SBT method to perform AST Traversal. We compare ComFormer with seven state-of-the-art baselines from code comment generation and neural machine translation domains. Comparison results show the competitiveness of ComFormer in terms of three performance measures. Moreover, we perform a human study to verify that ComFormer can generate high-quality comments., Comment: DSA2021

Until now, researchers have proposed several novel heterogeneous defect prediction HDP methods with promising performance. To the best of our knowledge, whether HDP methods can perform significantly better than unsupervised methods has not yet been thoroughly investigated. In this article, we perform a replication study to have a holistic look in this issue. In particular, we compare state-of-the-art five HDP methods with five unsupervised methods. Final results surprisingly show that these HDP methods do not perform significantly better than some of unsupervised methods (especially the simple unsupervised methods proposed by Zhou et al.) in terms of two non-effort-aware performance measures and four effort-aware performance measures. Then, we perform diversity analysis on defective modules via McNemar's test and find the prediction diversity is more obvious when the comparison is performed between the HDP methods and the unsupervised methods than the comparisons only between the HDP methods or between the unsupervised methods. This shows the HDP methods and the unsupervised methods are complementary to each other in identifying defective models to some extent. Finally, we investigate the feasibility of five HDP methods by considering two satisfactory criteria recommended by previous CPDP studies and find the satisfactory ratio of these HDP methods is still pessimistic. The above empirical results implicate there is still a long way for heterogeneous defect prediction to go. More effective HDP methods need to be designed and the unsupervised methods should be considered as baselines., Comment: 40 pages, 13 figures