+Artificial intelligence (AI) has become an integral part of the software development lifecycle and is rapidly transforming the way research software is developed, maintained, and applied across scientific disciplines. It offers powerful tools to improve the efficiency, quality, and scalability of research software and can assist with code generation, testing, documentation, debugging, and performance optimization, allowing developers to focus on higher-level design and scientific innovation. At the same time, machine learning models can automate repetitive tasks, detect patterns in large datasets, optimize computational workflows, and support decision-making. The growing integration of AI into research software also presents new opportunities and responsibilities.
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