US 12,141,549 B2
Method and apparatus for determining code generation quality and efficiency evaluation values based on multiple indicators
Ying Jiang, Yunnan (CN); Wenjun Yao, Yunnan (CN); Yang Yang, Yunnan (CN); Shouguo Tang, Yunnan (CN); Lingyu Li, Yunnan (CN); and Runxin Li, Yunnan (CN)
Assigned to Kunming University of Science and Technology, Kunming (CN)
Filed by Kunming University of Science and Technology, Yunnan (CN)
Filed on Jul. 26, 2022, as Appl. No. 17/873,178.
Claims priority of application No. 202111333853.3 (CN), filed on Nov. 11, 2021.
Prior Publication US 2023/0141348 A1, May 11, 2023
Int. Cl. G06F 8/30 (2018.01); G06F 8/41 (2018.01); G06F 18/22 (2023.01); G06F 40/14 (2020.01)
CPC G06F 8/30 (2013.01) [G06F 8/447 (2013.01); G06F 18/22 (2023.01); G06F 40/14 (2020.01)] 13 Claims
OG exemplary drawing
 
1. A method for determining code generation quality and efficiency evaluation values based on multiple indicators, comprising:
acquiring an object code, the object code comprising a first code input by a target object and a second code recommended by a code generation tool and determined by the target object;
acquiring an interactive data set, the interactive data set comprising behavioral interaction data between the target object and the code generation tool;
determining multidimensional evaluation indicators and extracting a plurality of characteristic values corresponding to each piece of data in the interactive data set according to the multidimensional evaluation indicators, wherein each characteristic value corresponds to an evaluation indicator of the each dimension one by one, the multidimensional evaluation indicators being configured to evaluate quality of generating the second code and efficiency of generating the second code;
determining the plurality of the characteristic values corresponding to each piece of the data in the interactive data set as a characteristic data set;
constructing a multidimensional syntax tree according to the object code and the characteristic data set;
performing feature learning on the multidimensional syntax tree by a convolutional neural network to obtain feature parameters; and
inputting the feature parameters to a value evaluation parameter model, and controlling the evaluation parameter model to output evaluation value, wherein the evaluation value comprises a first evaluation value and a second evaluation value, the first evaluation value being configured to evaluate the quality of generating the second code, and the second evaluation value being configured to evaluate the efficiency of generating the second code.