Deep learning-based java program internal annotation generation method and syste

A deep learning-based Java program internal annotation generation system, comprising:

• an annotation list extraction module, configured to:
• acquire items with a Stars number ranked in the top from GitHub, then extract method information in a Java source code with a static analysis tool JDT, then recognize corresponding method information, and extract corresponding internal annotations and method statement lists;
• an annotation-code pair extraction module, configured to:
• traverse statements in the method information according to positions of the annotations, obtain a statement list corresponding to the annotations through position information and data flow information, and obtain an <annotation, target code> pair;
• an annotation type filter module, configured to:
• perform filtration according to a type of the annotations and select an annotation in a Verb-dobj form;
• a code context generation module, configured to:
• analyze the data flow information implemented within the method information, and obtain a code context associated with a target code segment according to a definition use relationship between variables;
• a preprocessing module, configured to:
• preprocess the annotation, the target code, and the context, the preprocessing process comprising word segmentation, identifier splitting, and word lowercasing;
• and complete final processing to obtain a triplet dataset including <annotation, target code, code context>;
• a dataset dividing module, configured to:
• randomly divide the constructed dataset into a training set, a validation set, and a test set, and construct an encoder-decoder network at the same time;
• a model training module, configures to;
• enable the training set in division of the dataset dividing module to be used for model training, and perform evaluation on the validation set to obtain a model with the best effect on the validation set as a target model;
• and
• a predicted annotation generation module, configured to:
• predict data in the test set with the target model obtained by the model training module to generate a predicted annotation.