Feature Envy is a code smell due to the abnormal calling relationships between methods and classes, which adversely affects software scalability and maintainability.Existing methods mainly use various technologies to model abnormal relationships to detect feature envy.However, these methods only rely on local features such as entity names, which is not robust enough.Moreover, the mining depth of correlation features between entities involved in feature envy is limited.In this paper, we propose a correlation feature mining model based on dual attention to detect feature envy.Firstly, we propose a multi-view-based entity representation strategy, which enhanced the robustness of the model while improving the suitability of the correlation feature and model.Secondly, we add attention mechanism to the channel dimension and spatial dimension of CNN to control the flow of information and capture the correlation features between entities more accurately.Finally, the evaluation results on projects both with and without feature envy injected show that our proposed approach outperforms the state-of-the-art methods.

God class refers to a class that undertakes too many responsibilities for tasks that should more appropriately be handled by multiple classes. The existence of god classes seriously affects the maintainability and understandability of software. To eliminate god class, we first need to identify them. Researchers have proposed traditional methods using code metrics and deep learning methods using code metrics and text information to detect god classes. However, the relationship existing in metrics and text information is often ignored; moreover, deep learning methods require a large number of reliable datasets, while authentic god class datasets are scarce. To solve the above problems, we propose a novel god class detection method based on multi-aspect interactions and dataset fine-tuning. First, we use proposed model to extract multi-aspect interaction information, including three parts: (i) the interaction information existing in code metrics; (ii) the interaction information existing in texts; (iii) the interaction information existing in texts and code metrics. In this way, we can not only make use of code metrics and text information, but also fully exploit the multi-aspect interaction information. Second, we train with large-scale synthetic datasets to obtain a pre-trained model, then fine-tune the pre-trained model parameters with high-quality authentic datasets. Using the training method of pre-training and fine-tuning, we can solve the problem of low-reliability synthetic datasets and scarce authentic datasets. Finally, evaluation results on open-source applications suggest that the proposed approach improves on the state-of-the-art.