Patents by Inventor MICHELE TUFANO
MICHELE TUFANO has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Patent number: 11947935Abstract: Custom source code generation models are generated by tuning a pre-trained deep learning model by freezing the model parameters and optimizing a prefix. The tuning process is distributed across a user space and a model space where the embedding and output layers are performed in the user space and the execution of the model is performed in a model space that is isolated from the user space. The tuning process updates the embeddings of the prefix across the separate execution spaces in a manner that preserves the privacy of the data used in the tuning process.Type: GrantFiled: November 24, 2021Date of Patent: April 2, 2024Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.Inventors: Colin Bruce Clement, Neelakantan Sundaresan, Alexey Svyatkovskiy, Michele Tufano, Andrei Zlotchevski
-
Publication number: 20240104001Abstract: A debugging tool identifies the smallest subset of an input sequence or rationales that influenced a neural language model to generate an output sequence. The debugging tool uses the rationales to understand why the model made its predictions and in particular, the particular input tokens that had the most impact on the output sequence. In the case of erroneous output, the rationales are used to alter the input sequence to avoid the error or to tailor a new training dataset to retrain the model to improve its performance.Type: ApplicationFiled: December 15, 2022Publication date: March 28, 2024Inventors: COLIN BRUCE CLEMENT, DAVID ALBERTO NADER PALACIO, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO
-
Patent number: 11941373Abstract: A deep learning model trained to learn to predict source code is tuned for a target source code generation task through reinforcement learning using a reward score that considers the quality of the source code predicted during the tuning process. The reward score is adjusted to consider code-quality factors and source code metrics. The code-quality factors account for the predicted source code having syntactic correctness, successful compilation, successful execution, successful invocation, readability, functional correctness, and coverage. The source code metrics generate a score based on how close the predicted source code is to a ground truth code.Type: GrantFiled: December 17, 2021Date of Patent: March 26, 2024Assignee: Microsoft Technology Licensing, LLC.Inventors: Shao Kun Deng, Neelakantan Sundaresan, Alexey Svyatkovskiy, Michele Tufano
-
Publication number: 20240070053Abstract: An assert statement generator employs a neural transformer model with attention to generate candidate assert statements for a unit test method that tests a focal method. The neural transformer model is pre-trained with source code programs and natural language text and fine-tuned with test-assert triplets. A test-assert triplet includes a source code snippet that includes: (1) a unit test method with an assert placeholder; (2) the focal method; and (3) a corresponding assert statement. In this manner, the neural transformer model is trained to learn the semantics and statistical properties of a natural language, the syntax of a programming language, and the relationships between the code elements of the programming language and the syntax of an assert statement.Type: ApplicationFiled: October 23, 2023Publication date: February 29, 2024Inventors: DAWN DRAIN, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO
-
Patent number: 11893363Abstract: A unit test generation system employs a neural transformer model with attention to generate candidate unit test sequences given a focal method of a programming language. The neural transformer model is pre-trained with source code programs and natural language text and fine-tuned with mapped test case pairs. A mapped test case pair includes a focal method and a unit test case for the focal method. In this manner, the neural transformer model is trained to learn the semantics and statistical properties of a natural language, the syntax of a programming language and the relationships between the code elements of the programming language and the syntax of a unit test case.Type: GrantFiled: October 27, 2020Date of Patent: February 6, 2024Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.Inventors: Dawn Drain, Neelakantan Sundaresan, Alexey Svyatkovskiy, Michele Tufano
-
Patent number: 11829282Abstract: An assert statement generator employs a neural transformer model with attention to generate candidate assert statements for a unit test method that tests a focal method. The neural transformer model is pre-trained with source code programs and natural language text and fine-tuned with test-assert triplets. A test-assert triplet includes a source code snippet that includes: (1) a unit test method with an assert placeholder; (2) the focal method; and (3) a corresponding assert statement. In this manner, the neural transformer model is trained to learn the semantics and statistical properties of a natural language, the syntax of a programming language, and the relationships between the code elements of the programming language and the syntax of an assert statement.Type: GrantFiled: October 27, 2020Date of Patent: November 28, 2023Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.Inventors: Dawn Drain, Neelakantan Sundaresan, Alexey Svyatkovskiy, Michele Tufano
-
Publication number: 20230342287Abstract: A test-driven development system utilizes a neural transformer model with attention to generate method bodies for a focal method given its associated test cases, and optionally a method signature and a docstring of the focal method. The candidate method bodies are validated for syntactic correctness, tested using the given test cases, and tested with a donor class in a target system. Those candidate method bodies passing the validation and testing are then ranked based on a PLUM score that analyzes the candidate method bodies against various quality and performance metrics.Type: ApplicationFiled: June 19, 2023Publication date: October 26, 2023Inventors: COLIN BRUCE CLEMENT, SHAO KUN DENG, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO
-
Patent number: 11797426Abstract: A test-driven development system utilizes a neural transformer model with attention to generate method bodies for a focal method given its associated test cases, and optionally a method signature and a docstring of the focal method. The candidate method bodies are validated for syntactic correctness, tested using the given test cases, and tested with a donor class in a target system. Those candidate method bodies passing the validation and testing are then ranked based on a PLUM score that analyzes the candidate method bodies against various quality and performance metrics.Type: GrantFiled: October 22, 2021Date of Patent: October 24, 2023Assignee: MICROSOFT TECHNOLOGY LICENSINGInventors: Colin Bruce Clement, Shao Kun Deng, Neelakantan Sundaresan, Alexey Svyatkovskiy, Michele Tufano
-
Publication number: 20230251831Abstract: The syntax elements of a source code program used to represent the context of a focal method are selected based on a priority order. The selected syntax elements are input into a fixed-size context window that is used to train a neural transformer with attention model to learn to generate source code and used by the neural transformer model to generate source code. The context window contains prioritized sequences of tokens that extend beyond the target focus in order to provide a longer visibility back into the source code program for the model to learn predictive patterns. This gives the model a file-level context of the source code program without increasing the size of the context window.Type: ApplicationFiled: April 17, 2023Publication date: August 10, 2023Inventors: COLIN BRUCE CLEMENT, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO
-
Publication number: 20230195428Abstract: A deep learning model trained to learn to predict source code is tuned for a target source code generation task through reinforcement learning using a reward score that considers the quality of the source code predicted during the tuning process. The reward score is adjusted to consider code-quality factors and source code metrics. The code-quality factors account for the predicted source code having syntactic correctness, successful compilation, successful execution, successful invocation, readability, functional correctness, and coverage. The source code metrics generate a score based on how close the predicted source code is to a ground truth code.Type: ApplicationFiled: December 17, 2021Publication date: June 22, 2023Inventors: SHAO KUN DENG, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO
-
Patent number: 11656851Abstract: The syntax elements of a source code program used to represent the context of a focal method are selected based on a priority order. The selected syntax elements are input into a fixed-size context window that is used to train a neural transformer with attention model to learn to generate source code and used by the neural transformer model to generate source code. The context window contains prioritized sequences of tokens that extend beyond the target focus in order to provide a longer visibility back into the source code program for the model to learn predictive patterns. This gives the model a file-level context of the source code program without increasing the size of the context window.Type: GrantFiled: October 22, 2021Date of Patent: May 23, 2023Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.Inventors: Colin Bruce Clement, Neelakantan Sundaresan, Alexey Svyatkovskiy, Michele Tufano
-
Publication number: 20230128008Abstract: A test-driven development system utilizes a neural transformer model with attention to generate method bodies for a focal method given its associated test cases, and optionally a method signature and a docstring of the focal method. The candidate method bodies are validated for syntactic correctness, tested using the given test cases, and tested with a donor class in a target system. Those candidate method bodies passing the validation and testing are then ranked based on a PLUM score that analyzes the candidate method bodies against various quality and performance metrics.Type: ApplicationFiled: October 22, 2021Publication date: April 27, 2023Inventors: COLIN BRUCE CLEMENT, SHAO KUN DENG, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO
-
Publication number: 20230128200Abstract: The syntax elements of a source code program used to represent the context of a focal method are selected based on a priority order. The selected syntax elements are input into a fixed-size context window that is used to train a neural transformer with attention model to learn to generate source code and used by the neural transformer model to generate source code. The context window contains prioritized sequences of tokens that extend beyond the target focus in order to provide a longer visibility back into the source code program for the model to learn predictive patterns. This gives the model a file-level context of the source code program without increasing the size of the context window.Type: ApplicationFiled: October 22, 2021Publication date: April 27, 2023Inventors: COLIN BRUCE CLEMENT, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO
-
Publication number: 20230114423Abstract: An automated program repair tool utilizes a neural transformer model with attention to predict the contents of a bug repair in the context of source code having a bug of an identified bug type. The neural transformer model is trained on a large unsupervised corpus of source code using a span-masking denoising optimization objective, and fine-tuned on a large supervised dataset of triplets containing a bug-type annotation, software bug, and repair. The bug-type annotation is derived from an interprocedural static code analyzer. A bug type edit centroid is computed for each bug type and used in the inference decoding phase to generate the bug repair.Type: ApplicationFiled: November 25, 2022Publication date: April 13, 2023Inventors: SHAO KUN DENG, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO
-
Publication number: 20220398462Abstract: A cloud platform includes several web services that facilitate the automated tuning and deployment of pre-trained deep learning models configured for software engineering tasks. The automated tuning and deployment allow a developer to fine-tune a pre-existing model without having access to the parameters of the pre-existing and the fine-tuned model in a manner that does not require user management input. The cloud platform provides a set of files for each pre-trained models used to automatically build a fine-tuning infrastructure to fine-tune a model and a deployment infrastructure that deploys the fine-tuned model without requiring user input.Type: ApplicationFiled: June 14, 2021Publication date: December 15, 2022Inventors: COLIN BRUCE CLEMENT, SHAO KUN DENG, DAWN DRAIN, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, YIDING TIAN, MICHELE TUFANO, PAUL AN-CHIEH WANG, CHEN WU, DONGJIANG YOU
-
Patent number: 11526424Abstract: An automated program repair tool utilizes a neural transformer model with attention to predict the contents of a bug repair in the context of source code having a bug of an identified bug type. The neural transformer model is trained on a large unsupervised corpus of source code using a span-masking denoising optimization objective, and fine-tuned on a large supervised dataset of triplets containing a bug-type annotation, software bug, and repair. The bug-type annotation is derived from an interprocedural static code analyzer. A bug type edit centroid is computed for each bug type and used in the inference decoding phase to generate the bug repair.Type: GrantFiled: June 10, 2020Date of Patent: December 13, 2022Assignee: MICROSOFT TECHNOLOGY LICENSING LLC.Inventors: Shao Kun Deng, Neelakantan Sundaresan, Alexey Svyatkovskiy, Michele Tufano
-
Publication number: 20220066747Abstract: A unit test generation system employs a neural transformer model with attention to generate candidate unit test sequences given a focal method of a programming language. The neural transformer model is pre-trained with source code programs and natural language text and fine-tuned with mapped test case pairs. A mapped test case pair includes a focal method and a unit test case for the focal method. In this manner, the neural transformer model is trained to learn the semantics and statistical properties of a natural language, the syntax of a programming language and the relationships between the code elements of the programming language and the syntax of a unit test case.Type: ApplicationFiled: October 27, 2020Publication date: March 3, 2022Inventors: JAMES DRAIN, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO
-
Publication number: 20220066914Abstract: An assert statement generator employs a neural transformer model with attention to generate candidate assert statements for a unit test method that tests a focal method. The neural transformer model is pre-trained with source code programs and natural language text and fine-tuned with test-assert triplets. A test-assert triplet includes a source code snippet that includes: (1) a unit test method with an assert placeholder; (2) the focal method; and (3) a corresponding assert statement. In this manner, the neural transformer model is trained to learn the semantics and statistical properties of a natural language, the syntax of a programming language, and the relationships between the code elements of the programming language and the syntax of an assert statement.Type: ApplicationFiled: October 27, 2020Publication date: March 3, 2022Inventors: JAMES DRAIN, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO
-
Publication number: 20210357307Abstract: An automated program repair tool utilizes a neural transformer model with attention to predict the contents of a bug repair in the context of source code having a bug of an identified bug type. The neural transformer model is trained on a large unsupervised corpus of source code using a span-masking denoising optimization objective, and fine-tuned on a large supervised dataset of triplets containing a bug-type annotation, software bug, and repair. The bug-type annotation is derived from an interprocedural static code analyzer. A bug type edit centroid is computed for each bug type and used in the inference decoding phase to generate the bug repair.Type: ApplicationFiled: June 10, 2020Publication date: November 18, 2021Inventors: SHAO KUN DENG, NEELAKANTAN SUNDARESAN, ALEXEY SVYATKOVSKIY, MICHELE TUFANO