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The prediction of software defects and code smells is challenging, since it involves many factors inherent to the development process. Many studies propose machine learning models for defects and code smells. However, we have not found studies that explore and compare these machine learning models, nor that focus on the explainability of the models. This analysis allows us to verify which features and quality attributes influence software defects and code smells. Hence, developers can use this information to predict if a class may be faulty or smelly through the evaluation of a few features and quality attributes. In this study, we fill this gap by comparing machine learning models for predicting defects and seven code smells. We trained in a dataset composed of 19,024 classes and 70 software features that range from different quality attributes extracted from 14 Java open-source projects. We then ensemble five machine learning models and employed explainability concepts to explore the redundancies in the models using the top-10 software features and quality attributes that are known to contribute to the defects and code smell predictions. Furthermore, we conclude that although the quality attributes vary among the models, the complexity, documentation, and size are the most relevant. More specifically, Nesting Level Else-If is the only software feature relevant to all models.<\/jats:p>","DOI":"10.1007\/978-3-031-30826-0_16","type":"book-chapter","created":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T18:02:59Z","timestamp":1681927379000},"page":"282-305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Yet Another Model! 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