A Survey of Deep Learning Models for Structural Code Understanding

Abstract

In recent years, the rise of deep learning and automation requirements in the software industry has elevated Intelligent Software Engineering to new heights. The number of approaches and applications in code understanding is growing, with deep learning techniques being used in many of them to better capture the information in code data. In this survey, we present a comprehensive overview of the structures formed from code data. We categorize the models for understanding code in recent years into two groups: sequence-based and graph-based models, further make a summary and comparison of them 1 . We also introduce metrics, datasets and the downstream tasks. Finally, we make some suggestions for future research in structural code understanding field.

Application of Bayesian networks for the prediction of gas accidents in semi-mechanized underground operations on the southern coast of Peru

“…In this way, with a sensitivity value of 50% and by applying the ROC formula, the specificity value must be obtained to classify the inference. [20] He established that this probabilistic parameter makes it possible to identify whether a prediction is correct (standard positive) or not correct (false positive).…”