With Python's ascension as a dominant program-ming language, particularly in the fields of artificial intelligence and data science, the need for comprehensive datasets focusing on software quality within Python projects has become increasingly noticeable. This study introduces a detailed dataset designed to address this gap, enriching academic resources in software engineering. The dataset encompasses a wide array of software quality metrics on up to 80 projects, including 51.765.853 Sonar-Qube issues, 268.506 SonarQube code quality metrics, 11.915 software refactoring records, and 155.127 pairs of bug-inducing and bug-fixing commits, along with 863.931 GitHub issue tracker entries. This extensive collection serves as a versatile tool for various research activities, enabling analysis of the relationships between technical debt and software refactorings, correlations be-tween refactoring processes and bug resolution, and their overall impact on software maintainability and reliability. By offering a comprehensive and multifaceted dataset, this study significantly contributes to understanding and improving software quality in Python projects.

Refactoring is an important process in software engineering, aiming to improve code quality without altering the behavior. This article presents a systematic review of the literature (SLR) on artificial intelligence in the domain of software refactoring. Following a rigorous methodology consisting of data extraction, snowballing techniques, and manual validation, we created a dataset consisting of 156 articles. The focus of the investigation was to identify the refactoring stages that are addressed. The results show that, as research type, most of the contributions propose solutions, while other forms of research such as evaluation, validation and experience are less represented in publications. Refactoring detection represents the highest interest in research contributions, while other refactoring stages, such as prioritization or testing are less investigated. The most commonly used AI methods include Random Forests, Genetic Algorithms, SVM, CNNs and Decision Trees. Based on this literature review, we have identified research trends and opportunities for future research.