Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code
Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has enabled state-of-the-art results to be achieved on a wide variety of SE tasks. This paper provides an overview of this rapidly advancing field of research and reflects on future research directions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Software Performance Engineering for Foundation Model-Powered Software (FMware)
The rise of Foundation Models (FMs) like Large Language Models (LLMs) is revolutionizing software development. Despite the impressive prototypes, transforming FMware into production-ready products demands complex enginee…
A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective
In this survey paper, we overview major deep learning methods used in Natural Language Processing (NLP) and source code over the last 35 years. Next, we present a survey of the applications of Artificial Intelligence (AI…
SurveyOptimization meets Big Data: A survey
This paper reviews recent advances in big data optimization, providing the state-of-art of this emerging field. The main focus in this review are optimization techniques being applied in big data analysis environments. I…
SurveyDialogue Systems Engineering: A Survey and Future Directions
This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With th…
Machine Learning Application Development: Practitioners' Insights
Nowadays, intelligent systems and services are getting increasingly popular as they provide data-driven solutions to diverse real-world problems, thanks to recent breakthroughs in Artificial Intelligence (AI) and Machine…
BIG-bench Machine Learning