Deep Learning (DL) frameworks play a critical role in advancing artificial intelligence, and their rapid growth underscores the need for a comprehensive understanding of software quality and maintainability. DL frameworks, like other systems, are prone to code clones. Code clones refer to identical or highly similar source code fragments within the same project or even across different projects. Code cloning can have positive and negative implications for software development, influencing maintenance, readability, and bug propagation. While the existing studies focus on studying clones in DL-based applications, to our knowledge, no work has been done investigating clones, their evolution and their impact on the maintenance of DL frameworks. In this paper, we aim to address the knowledge gap concerning the evolutionary dimension of code clones in DL frameworks and the extent of code reuse across these frameworks. We empirically analyze code clones in nine popular DL frameworks, i.e., TensorFlow, Paddle, PyTorch, Aesara, Ray, MXNet, Keras, Jax and BentoML, to investigate (1) the characteristics of the long-term code cloning evolution over releases in each framework, (2) the short-term, i.e., within-release, code cloning patterns and their influence on the long-term trends, and (3) the file-level code clones within the DL frameworks. Our findings reveal that DL frameworks adopt four distinct cloning trends: “Serpentine”, “Rise and Fall”, “Decreasing”, and “Stable” and that these trends present some common and distinct characteristics. For instance, bug-fixing activities persistently happen in clones irrespective of the clone evolutionary trend but occur more in the “Serpentine” trend. Moreover, the within-release level investigation demonstrates that short-term code cloning practices impact long-term cloning trends. The cross-framework code clone investigation reveals the presence of functional and architectural adaptation file-level cross-framework code clones across the nine studied frameworks. We provide insights that foster robust clone practices and collaborative maintenance in the development of DL frameworks.

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It was a pleasure participating in the Tollab event, organized by Tollab: la fédération des étudiants libanais à Montréal. This incredible initiative unites the Lebanese student associations of McGill, Concordia, Université de Montréal, and Polytechnique Montréal, fostering a strong sense of community and support among students from all academic backgrounds. The event provided a valuable platform for students to engage with professionals from various fields, gain insights into life after university, and be inspired to reach their full potential. As someone with expertise in computer science, I was delighted to share my experiences and connect with students eager to explore career opportunities in tech and beyond. A big thank you to the organizers and participants for making this event such a success! Looking forward to more opportunities to support and uplift the next generation of students. To learn more about Tollab and their initiatives, feel free to check out their website: www.tollab.info

I'm thrilled to announce that I have joined the SE in MTL community under the SEMTL group! This vibrant network is all about fostering connections and collaboration among professionals in the software engineering space here in Montreal. I had the fantastic opportunity to participate in the November session of the SEMTL Meeting at UQÀM, where I connected with like-minded researchers, shared insights, and research ideas. Stay tuned for more updates on my journey with SEMTL upcoming events!