Abstract: Techniques for multi-layer training of a machine learning model are disclosed. A system pre-trains a machine learning model on training data obtained from unlabeled document graph data by executing unsupervised pre-training tasks on the unlabeled document graph data to generate a labeled pre-training data set. The system modifies document graphs to change attributes of nodes in the document graphs. The system pre-trains the machine learning model with a data set including the modified document graphs and un-modified document graphs to generate prediction associated with the modifications to the document graphs. Subsequent to pre-training, the system fine-tunes the machine learning model with a set of labeled training data to generate predictions associated with a specific attribute of a document graph.

Abstract: A system accesses a code module that includes one or more units of code and instructs a machine learning model to generate a test suite based on a specification for the code module. The test suite includes tests for testing the code module to verify that one or more units of code successfully execute in accordance with the specification. The specification includes preconditions that precede successful execution of the one or more units of code and postconditions that exist following successful execution of the one or more units of code. The machine learning model generates the test suite. The system receives the test suite from the machine learning model. The system stores and/or transmits the test suite for use in testing the code module.

Abstract: Techniques for multi-layer training of a machine learning model are disclosed. A system pre-trains a machine learning model on training data obtained from unlabeled document graph data by executing unsupervised pre-training tasks on the unlabeled document graph data to generate a labeled pre-training data set. The system modifies document graphs to change attributes of nodes in the document graphs. The system pre-trains the machine learning model with a data set including the modified document graphs and un-modified document graphs to generate prediction associated with the modifications to the document graphs. Subsequent to pre-training, the system fine-tunes the machine learning model with a set of labeled training data to generate predictions associated with a specific attribute of a document graph.