In a very recent paper by Qu et al. (IEEE Transactions on Software Engineering, vol. 47 no. 2, pp. 348-366, Feb. 1 2021, doi:10.1109/TSE.2019.2892959), the authors propose an effective equation, top-core, to improve the performance of effort-aware bug prediction models. A distinctive feature of top-core is that it takes into account the coreness of a class in a Class Dependency Network (CDN) when calculating the relative risk of a class to be buggy. In this comment, we show that Qu et al.'s paper contains three shortcomings that may influence the performance of top-core or even have the potential to lead to erroneous results. First, we show that the CDN that they use to calculate the coreness of classes is not very accurate, neglecting many important types of dependency relations between classes such as method call relation, access relation, and instantiates relation. Second, they trained a Logistic Regression model using the scikit-learn framework to predict the probability of a specific class to be buggy. It is actually an L2 regularized Logistic Regression model, which is dependent on the scale of the features. But they neglected to normalize the features, making the obtained results erroneous. Finally, the number of execution times (viz. 10 times in the paper of Qu et al.) they used to reduce the bias caused by the randomness (viz. random split of instances and the process to handle class-imbalance problem) in the experiments is too small to ensure that the obtained results converge to stable values; but they failed to signify the precision level of their results for comparison. In this comment, we provide solutions to the problems by using i) an improved CDN (ICDN) to represent the structure of software systems, ii) the z-score method to normalize the features, and iii) an adaptive mechanism to determine the number of execution times. In the experiments, we find that Qu et al.'s approach based on the Logistic Regression model does not perform significantly better than the state-of-the-art approach Ree, which is inconsistent with the conclusion in Qu et al.'s work. We also observe that replacing CDN with ICDN does improve the performance of Qu et al.'s approach.