id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
bracis-28401	Pfitscher, Ricardo J.; Rodenbusch, Gabriel B.; Dias, Anderson; Vieira, Paulo; Fouto, Nuno M. M. D.	Estimating Code Running Time Complexity with Machine Learning	2023		.htm	text/html	6295	309	49	However, the recent advances in artificial intelligence propelled the development of models that estimate code complexity. Second, it shows that the Random Forest model achieved the best results for predicting code complexity, with an accuracy of 71.84% using code features as attributes and 83.57% when Abstract Syntax Tree (AST) applies to generate code embedding used in training.	cache/bracis-28401.htm	txt/bracis-28401.txt