To deal with continuous change requests and the strict time-To-market, practitioners and big companies constantly update their software systems to meet users requirements. This practice force developers to release immature products, neglecting best practices to reduce delivery times. As a possible result, technical debt can arise, i.e., potential design issues that can negatively impact software maintenance and evolution and, in turn, increase both the time-To-market and costs. Code smells-sub-optimal design decisions identifiable by computing software metrics and providing a general overview of code quality-Are common symptoms of technical debt. While previous research focused on code smells primarily considering them in the context of Java, the growing popularity of Python, particularly for developing artificial intelligence (AI)-Enabled systems, calls for additional investigations. This preliminary analysis addresses this gap by exploring the diffusion of Python-specific code smells, and the activities performed by developers that induce the introduction of code smells in their systems. To perform our preliminary investigation, we selected 200 AI-Enabled systems available in the Niche dataset; We extracted 10,611 information on the releases using PyDriller, and PySmell to extract information about code smells. The results reveal several insights: 1) Code smells related to object-oriented principles are rarely detected in Python; 2) Complex List Comprehension is the most prevalent and the most long-Alive smell; 3) The main activities that can induce code smells are evolutionary. This study fills a critical gap in the literature by providing empirical evidence on the evolution of code smells in Python-based AI-enabled systems.
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