Microsoft Technology Licensing, LLC

Grirem Advanced Materials Co., Ltd.

Shuai Lu

Neelakantan Sundaresan

Alexey Svyatkovskiy

Colin Bruce Clement

Nan Duan

Shengyu Fu

Duyu Tang

Hongwei Li

Yang Luo

Dunbo Yu

Jiajun Xie

Wenlong Yan

Kuoshe Li

A large language model, previously pre-trained on multiple source code modeling tasks, is pre-trained, through curriculum learning, to learn to predict a code execution trace given a source code program. The model is pre-trained using a variety of pre-training datasets consisting of pairs of a source code sample and a corresponding execution trace. The curriculum pre-training starts with a pre-training dataset of single line executions and adds in additional pre-training datasets with more increasing complex behaviors. The pre-training datasets include mutation-augmented source code samples and their corresponding execution traces.

Code execution trace generation with pre-trained large language model

A deep learning model is pre-trained with a large-scale of unsupervised data of code review tasks in order to learn the relationships between code changes and a code review. The pre-trained deep learning model predicts a code review given a code diff hunk in a code diff format. The code diff hunk includes the changed code and its surrounding context. The pre-trained deep learning model may then be fine-tuned with supervised data in order to make predictions for several code review activities, such as, code change quality estimation and code refinement.

Pre-training for automating code review activities

A retrieval-augmented code completion system uses the context of a partially-formed source code snippet of a source code program and a hint to predict the source code tokens needed to complete the partially-formed source code snippet. The hint is a source code segment that completes a semantically-similar source code segment of the partially-formed source code snippet. The hint is found in a retrieval source code database using a hybrid retrieval technique. A deep learning decoder model uses the context of the partially-formed source code snippet and the hint to predict the most likely candidate sequence of source code tokens to complete the partially-formed source code snippet.

Retrieval-augmented code completion

A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

Multi-lingual code generation with zero-shot inference

The application discloses a rare-earth permanent magnetic powder, a bonded magnet, and a device using the bonded magnet. The rare-earth permanent magnetic powder comprises 4 to 12 at. % of Nd, 0.1 to 2 at. % of C, 10 to 25 at. % of N and 62.2 to 85.9 at. % of T, wherein T is Fe or FeCo and the main phase of the rare-earth permanent magnetic powder is a hard magnetic phase with a TbCustructure. Material volatilization can be avoided effectively during a preparation process of the rare earth permanent magnetic powder, thus improving the wettability with a water-cooling roller during the preparation process and final prepared materials are provided with good magnetic properties.

Rare earth permanent magnetic powder, bonded magnet and device using the bonded magnet

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Beijing, China

Shuai Lu