Automated Program Synthesis and Optimization Using Foundation Models in Software Engineering

Abstract

Synthesis and optimization of programs run on computers have become two key research topics in the current software engineering. The emergence of foundation models, which are trained on massive pretexts on large code and natural language datasets, presents new possibilities in interpreting, creating and optimizing code. The paper explores the use of foundation models in the synthesis of programs in a self-managed manner and in enhancing the performance of programs. We recommend a structural approach that incorporates deep learning models into existing software engineering pipelines to produce, execute, and improve code in an automated approach. The given framework uses transformer based models to represent codes, understand them and predict errors. Empirical experience has shown that foundation model-based synthesis can save a great deal of development time, result in better code quality, and higher accuracy as compared to the conventional heuristic-based methods. Besides, the framework offers information on optimization strategies such as minimization of computational resources and energy consumption. Based on an analysis we also draw attention to challenges that may be faced, such as model interpretability, data bias and scalability, which provides avenues upon which future research may be based in regard to developing intelligent software. This paper gives a broad outlook of how foundation models can be exploited to provide automated program synthesis and optimization, and offers viable implications to the academic and industry sectors