Abstract:

With the continuous development of space technology, space control software is facing increasingly complex business forms and continuously changing business needs. Software shows the trend of increasingly complex functions, larger scale, shorter development cycle, etc. Software systems become increasingly large and difficult to control. When engineers face new software requirements, they tend to find the most similar function points in the historical model tasks for software code inheritance, so as to reduce development costs and improve development efficiency. In the face of massive code base, it is difficult for code retrieval methods to obtain the requirements and codes related to intent efficiently and accurately, which affects the efficiency and quality of software development and restricts the efficient and reliable delivery of software. In order to solve the above problems, this paper proposes an artificial intelligence(AI)-based code retrieval method for space control software. Based on semantic vector model, this method extracts and processes documents and codes in historical assets of type control software, constructs a mapping relationship between function codes and requirements, and constructs a retrieval library to provide support for the code. This paper focuses on the practical application of AI enabled industrial software design to optimize software development process, improve development efficiency and quality, and enhance intelligent capability. It provides effective solutions to solve the problem of efficient utilization of existing assets in many research and development scenarios in the industrial industry, and provides strong support for promoting the high-quality development of intelligent manufacturing in China.

Key words: AI, semantic vector model, space control software, code search