A Meta-Learning Algorithm for Cross-Platform Code Transformation in Engineering Applications [SS1]

Abstract

Ensuring compatibility across multiple platforms is still a prominent issue in software engineering, especially in
systems which have embedded systems, high-performance computing clusters, and cloud computing. This paper aims
to solve the issue by implementing a meta-learning algorithm intended for cross-platform code augmentation and
automation. A meta-learning architecture paired with a domain-specific knowledge system is designed to make guided
decision transformations and relies on past experiences which are from insufficiently similar tasks. The algorithm is
designed for swift task execution by deploying few-shot learning and model-agnostic meta-learning (MAML) which
reduces retraining, thus making the algorithm efficient and scalable. We provide a comprehensive system architectural
design with components which feature the code representation module, the transformation policy learner, and the
performance feedback loop. The module for representing codes implements a graph-based algorithm that learns and
extracts formal and meaning patterns from the source codes which allows for boundless abstraction on different
platforms. The transformer policy learner refines strategies by leveraging feedback from the system memories, runtime
efficiencies, and energy consumption using reinforcement and meta-learning. Experimental results validate the
effectiveness of the proposed algorithm that outperformed existing tools in ARM, x86, and CUDA platform
transformation by up to 30%. Moreover, the model demonstrates strong generalization capability by performing
effectively on novel code patterns and new engineering domains. We cover the effects of meta-learning on
maintainability, human effort in cross-platform development, and potential integration with software development
environments. This adds to the existing literature in the fusion of machine learning with programming languages and
systems engineering by providing a solution to an emerging concern in modern software engineering. We emphasize
the role of meta-learning not just in the automation of intricate engineering processes, but in the prospective
transformation of self-modifying agile systems, which reconfigures system structures to optimize functionality.