A review on statistical language and neural network based code completion

Abstract

Code completion, also referred to as intellisense, is a prevalent feature of Integrated Development Environments (IDEs) and code editors. It aids developers by automatically recommending and inserting code segments, variable names, and method names, among other things. With the accelerated growth of the software industry and the process of digitalization in recent years, the demand for software engineers has reached a record-high level. Thus, the advancement of code completion is encouraged and has become a popular topic in software engineering. This paper examines and summarizes the development of a statistical language and neural network-based code completion system. The main contents consist of introducing the concepts of code completion system, summarizing the general process of code completion and the evaluation metrics used for performance benchmarking, reviewing and summarizing the existing work conducted on statistical language approach and neural network approach respectively, as well as the limitations and challenges of existing code completion method, and finally forecasting the future development of code completion techniques.