Performance Evaluation Method of Code Generation Based on Semisupervised Learning
ZHANG Xiao-jiang，JIANG Ying
（Yunnan Key Lab of Computer Technology Application，Kunming 650500，China）（Faculty of Information Engineering and Automation，Kunming University of Science and Technology，Kunming 650500，China）
Abstract：In order to improve the quality and efficiency of software development，automatic code generation is the current research hotspot.The performance of automatic code generation is an important issue.The existing performance analysis methods of automatic code generation are relatively simple，so it is difficult to evaluate the characteristics of programmers and automatic code generation tools in the process of automatic code generation.In this paper，the function of the programmer and the automatic code generation tool in the process of automatic code generation is considered.A method of evaluating the performance of automatic code generation based on semisupervised learning is proposed.By extracting the important characteristics from the behavior of the programmer and the automatic code generation tool，the performance category of automatic code generation is divided.Then the performance evaluation model of automatic code generation process based on Deep Neural Networks is established.Finally，the impact on performance produced by behavior both programmers and automatic code generation tools is calculated.Experimental results show that this method can effectively analyze the impact on the performance from programmer behavior and automatic code generation tool behavior during the process of code generation.
2021, Vol. 42 
