AI-ASSISTED CODE GENERATION AND OPTIMIZATION: LEVERAGING MACHINE LEARNING TO ENHANCE SOFTWARE DEVELOPMENT PROCESSES
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Abstract
The aim of this paper is to explore the AI-Driven Code Generation and Optimization. The continuous evolution of code generators has also opened up new possibilities for automating repetitive tasks, allowing for greater focus on high-level problem-solving and design rather than low-level implementation details. As technology continues to advance, the role of code generators in software development is expected to expand even further, offering innovative solutions to the challenges of tomorrow's computing landscape. Whether the ultimate vision is that of a programmer taking "creative coding" to the next level, the expert use of specialized DSLs, or automated software development, we believe that the pathway to practical realization is indeed in their enclosure [1]. Within this context, it is evident that the integration of AI methodologies and flex space technologies is a significant area of interest for researchers, as it presents numerous opportunities for continued innovation and advancement in the field. As we delve deeper into the intricacies of code generation and optimization techniques, it becomes increasingly apparent that further exploration and refinement are crucial in order to unlock the full potential of these cutting-edge AI-driven approaches. Additionally, the identification and proactive mitigation of challenges inherent in the convergence of AI and flex space are pivotal to ensuring the successful development and deployment of impactful solutions. Through a nuanced understanding of these critical domains, researchers and practitioners can work towards realizing the transformative possibilities that lie at the intersection of AI and flex space technologies [1]. By addressing the complexities and nuances associated with these advanced methodologies, we can facilitate the evolution of programming practices and software development processes, ultimately leading to the materialization of the envisioned creative and efficient computing ecosystems.
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