基于真值表的函数自动生成的神经网络模型OACSTPCD

Programming by examples is a common program synthesis problem that involves generating programs based on input/output examples provided by users,it offers convenience for novice programmers.Recently,programming by examples has been applied to automatic programming of Microsoft Office Excel,as well as in exploration,logging,and aerospace.In order to address the gap resulting from limited research on binary data flow,the problem of gen-erating function expressions based on truth tables is introduced.This problem has the characteristic that the rela-tionship between the syntactic symbols in the sequence of symbols in the function expression is independent of their distances.Additionally,the generation of semantic rules for the function expression is unrelated to the resulting length of the Boolean vector function sampling.Based on the aforementioned characteristics,this paper introduces a neural network model and algorithm that achieve results of 70.56%,64.66%,and 0.6355 in program synthesis,functional equivalence,and sequence matching,respectively.These results outperform the existing state-of-the-art program synthesis model,which achieves 55.07%,49.70%,and 0.569 0,respectively.