Patents by Inventor ROSHANAK ZILOUCHIAN MOGHADDAM
ROSHANAK ZILOUCHIAN MOGHADDAM 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).
-
Publication number: 20240118967Abstract: A failure recommendation system for a command line interface (CLI) uses machine learning to predict the most likely command to correct an unsuccessful or failed attempt to perform an intended operation using the CLI. The failure recommendation system is based on a conditional probability model trained on failure-success pairs of commands from CLI telemetry data to learn the most likely command to remediate a failure. The conditional probability model predicts the most likely command based on a failure type and the failed command. The failure type is identified through a failure type classifier and is used to select the most likely command to remediate a failure from the different events that may lead to a failure.Type: ApplicationFiled: November 30, 2023Publication date: April 11, 2024Inventors: CHRISTOPHER O'TOOLE, ROSHANAK ZILOUCHIAN MOGHADDAM
-
Publication number: 20240028740Abstract: A neural classifier model is used to detect cybersecurity vulnerabilities in the source code predicted by a deep learning code generation model having been trained on source code possibly containing security bugs. Upon the classifier model classifying a given source code snippet as likely containing a cybersecurity vulnerability, a proposed repair for the cybersecurity vulnerability is predicted from a neural decoder transformer model having been trained on non-vulnerable source code. The neural decoder transformer model is used to predict source code that repairs the cybersecurity vulnerability given the source code classified with a cybersecurity vulnerability.Type: ApplicationFiled: September 21, 2022Publication date: January 25, 2024Inventors: AARON YUE-CHIU CHAN, COLIN BRUCE CLEMENT, YEVHEN MOHYLEVSKYY, NEELAKANTAN SUNDARESAN, ROSHANAK ZILOUCHIAN MOGHADDAM
-
Patent number: 11860725Abstract: A failure recommendation system for a command line interface (CLI) uses machine learning to predict the most likely command to correct an unsuccessful or failed attempt to perform an intended operation using the CLI. The failure recommendation system is based on a conditional probability model trained on failure-success pairs of commands from CLI telemetry data to learn the most likely command to remediate a failure. The conditional probability model predicts the most likely command based on a failure type and the failed command. The failure type is identified through a failure type classifier and is used to select the most likely command to remediate a failure from the different events that may lead to a failure.Type: GrantFiled: November 22, 2020Date of Patent: January 2, 2024Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.Inventors: Christopher O'Toole, Roshanak Zilouchian Moghaddam
-
Publication number: 20230281317Abstract: A false positive vulnerability system detects whether a software vulnerability identified by a static code vulnerability analyzer is a true vulnerability or a false positive. The system utilizes deep learning models to predict whether an identified vulnerability is accurate given the source code context of the identified vulnerability. A neural encoder transformer model is trained to classify a false positive given the method body including the identified vulnerability. A neural decoder transformer model is trained to predict a candidate line-of-code to complete a prompt inserted into the context of the identified vulnerability. The candidate line-of-code that successfully completes the prompt is used as a signal to identify that the identified vulnerability is a false positive.Type: ApplicationFiled: March 4, 2022Publication date: September 7, 2023Inventors: COLIN BRUCE CLEMENT, MATTHEW GLENN JIN, ANANT GIRISH KHARKAR, XIAOYU LIU, XIN SHI, NEELAKANTAN SUNDARESAN, ROSHANAK ZILOUCHIAN MOGHADDAM
-
Publication number: 20230153226Abstract: A computer implemented method includes accessing performance trace data for executed code of multiple services. Symbols corresponding to functions of the executed code are identified. First sequences of functions from the identified symbols are identified and a first performance threshold for each identified first sequence of functions is computed. The method includes receiving an incoming performance trace, detecting second sequences of functions from the incoming performance trace, identifying second sequences equivalent to the first sequences, and comparing performance of the identified second sequences to the first performance threshold for each of the equivalent first sequences to identify second sequences as comprising a performance bottleneck.Type: ApplicationFiled: November 12, 2021Publication date: May 18, 2023Inventors: Spandan Garg, Roshanak Zilouchian Moghaddam, Paul Sean Harrington, Chen Wu, Neelakantan Sundaresan
-
Patent number: 11640294Abstract: Examples of the usage of a command of a command line interface includes the command with a set of parameters and corresponding parameter values. The examples are generated from telemetry data, which does not contain parameter values, and from web-based sources that may contain multiple parameter values. A machine learning model is used to predict the data type of a parameter value when the parameter is used with a particular command. The predicted data type is then used to select an appropriate parameter value for the example from multiple known parameter values or to generate a parameter value when no known parameter value exists.Type: GrantFiled: April 29, 2020Date of Patent: May 2, 2023Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.Inventors: Spandan Garg, Jason R. Shaver, Neelakantan Sundaresan, Roshanak Zilouchian Moghaddam
-
Publication number: 20230073052Abstract: A code completion system for a CLI utilizes neural transformer models with attention to generate candidates to complete a line of CLI code. The code completion system uses a first deep learning model to predict at most k candidate command names to follow n immediately preceding lines of CLI code which are presented to a developer. Upon the developer accepting one of the candidate command names, the code completion system uses a second deep learning model to predict at most k parameter strings to complete the line of CLI code.Type: ApplicationFiled: September 1, 2021Publication date: March 9, 2023Inventors: YEVHEN MOHYLEVSKYY, ALEXEY SVYATKOVSKIY, ROSHANAK ZILOUCHIAN MOGHADDAM
-
Patent number: 11599447Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.Type: GrantFiled: July 4, 2022Date of Patent: March 7, 2023Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.Inventors: Shaun Miller, Kalpathy Sitaraman Sivaraman, Neelakantan Sundaresan, Yijin Wei, Roshanak Zilouchian Moghaddam
-
Patent number: 11561810Abstract: An automated command assistance tool is provided for a browser-enabled command line interface of a cloud service. The automated command assistance tool provides examples illustrating the correct syntax for commands used to manage the resources of a cloud service. The command assistance tool learns the syntax of a command from usage patterns found in telemetric data, scripts and user documentation and forms templates containing a command's usage pattern and related information. The templates are used to generate examples that respond to a user query for assistance with usage of a particular command.Type: GrantFiled: January 11, 2019Date of Patent: January 24, 2023Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.Inventors: Roshanak Zilouchian Moghaddam, Neelakantan Sundaresan, Jason Shaver
-
Publication number: 20220342800Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.Type: ApplicationFiled: July 4, 2022Publication date: October 27, 2022Inventors: SHAUN MILLER, KALPATHY SITARAMAN SIVARAMAN, NEELAKANTAN SUNDARESAN, YIJIN WEI, ROSHANAK ZILOUCHIAN MOGHADDAM
-
Patent number: 11436236Abstract: A term-weighting and document-scoring function is used to search for a command line interface (CLI) script that is likely relevant to an operation specified in a natural language query. CLI scripts are created to perform various operations of a CLI-based application. A CLI script is associated with a description document having keywords associated with the individual commands used in the CLI script. The relevance of a CLI script to an intended operation is based on the term-weighting and document-scoring function which is applied to each component of each command in a CLI script and weighted accordingly.Type: GrantFiled: May 1, 2020Date of Patent: September 6, 2022Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Spandan Garg, Yevhen Mohylevskyy, Jason R. Shaver, Neelakantan Sundaresan, Roshanak Zilouchian Moghaddam
-
Publication number: 20220253712Abstract: An example generator tool generates an example illustrating correct usage of a command of a command line interface. A command may include a command name, zero or more subcommands, and one or more parameters with a corresponding parameter value. A template containing the correct syntax of the command is obtained from a template database. Parameter values for the template are generated from a neural transformer with attention given the command template.Type: ApplicationFiled: April 19, 2021Publication date: August 11, 2022Inventors: COLIN BRUCE CLEMENT, ROSHANAK ZILOUCHIAN MOGHADDAM, NEELAKANTAN SUNDARESAN
-
Patent number: 11403207Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.Type: GrantFiled: February 28, 2020Date of Patent: August 2, 2022Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.Inventors: Shaun Miller, Kalpathy Sitaraman Sivaraman, Neelakantan Sundaresan, Yijin Wei, Roshanak Zilouchian Moghaddam
-
Publication number: 20220222165Abstract: An automated system for detecting performance bugs in a program and for providing code recommendations to improve the performance of the program generates a code recommendation table from performance-related pull requests. The performance-related pull requests are identified in part from a classifier trained on semi-supervised data. A code recommendation table is generated from performance-related pull requests and is searched for similarly-improved code based on a set of difference features that includes structural and performance features of the before-code of a pull request that is not in the after-code.Type: ApplicationFiled: March 11, 2021Publication date: July 14, 2022Inventors: SPANDAN GARG, PAUL SEAN HARRINGTON, CHEN WU, ROSHANAK ZILOUCHIAN MOGHADDAM
-
Publication number: 20220091923Abstract: A failure recommendation system for a command line interface (CLI) uses machine learning to predict the most likely command to correct an unsuccessful or failed attempt to perform an intended operation using the CLI. The failure recommendation system is based on a conditional probability model trained on failure-success pairs of commands from CLI telemetry data to learn the most likely command to remediate a failure. The conditional probability model predicts the most likely command based on a failure type and the failed command. The failure type is identified through a failure type classifier and is used to select the most likely command to remediate a failure from the different events that may lead to a failure.Type: ApplicationFiled: November 22, 2020Publication date: March 24, 2022Inventors: CHRISTOPHER O'TOOLE, ROSHANAK ZILOUCHIAN MOGHADDAM
-
Patent number: 11250038Abstract: An interactive question and answer (Q&A) service provides pairs of questions and corresponding answers related to the content of a web page. The service includes pre-configured Q&A pairs derived from a deep learning framework that includes a series of neural networks trained through joint and transfer learning to generate questions for a given text passage. In addition, pre-configured Q&A pairs are generated from historical web access patterns and sources related to the content of the web page.Type: GrantFiled: August 13, 2018Date of Patent: February 15, 2022Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.Inventors: Payal Bajaj, Gearard Boland, Anshul Gupta, Matthew Glenn Jin, Eduardo Enrique Noriega De Armas, Jason Shaver, Neelakantan Sundaresan, Roshanak Zilouchian Moghaddam
-
Publication number: 20210342357Abstract: A term-weighting and document-scoring function is used to search for a command line interface (CLI) script that is likely relevant to an operation specified in a natural language query. CLI scripts are created to perform various operations of a CLI-based application. A CLI script is associated with a description document having keywords associated with the individual commands used in the CLI script. The relevance of a CLI script to an intended operation is based on the term-weighting and document-scoring function which is applied to each component of each command in a CLI script and weighted accordingly.Type: ApplicationFiled: May 1, 2020Publication date: November 4, 2021Inventors: SPANDAN GARG, YEVHEN MOHYLEVSKYY, JASON R. SHAVER, NEELAKANTAN SUNDARESAN, ROSHANAK ZILOUCHIAN MOGHADDAM
-
Publication number: 20210342654Abstract: Examples of the usage of a command of a command line interface includes the command with a set of parameters and corresponding parameter values. The examples are generated from telemetry data, which does not contain parameter values, and from web-based sources that may contain multiple parameter values. A machine learning model is used to predict the data type of a parameter value when the parameter is used with a particular command. The predicted data type is then used to select an appropriate parameter value for the example from multiple known parameter values or to generate a parameter value when no known parameter value exists.Type: ApplicationFiled: April 29, 2020Publication date: November 4, 2021Inventors: SPANDAN GARG, JASON R. SHAVER, NEELAKANTAN SUNDARESAN, ROSHANAK ZILOUCHIAN MOGHADDAM
-
Publication number: 20210271587Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.Type: ApplicationFiled: February 28, 2020Publication date: September 2, 2021Inventors: SHAUN MILLER, KALPATHY SITARAMAN SIVARAMAN, NEELAKANTAN SUNDARESAN, YIJIN WEI, ROSHANAK ZILOUCHIAN MOGHADDAM
-
Patent number: 10671355Abstract: A code completion tool uses machine learning models to more precisely predict the likelihood of a method invocation completing a code fragment that follows one or more method invocations of a same class in a same document during program development. In one aspect, the machine learning model is a n-order Markov chain model that is trained on features that represent characteristics of the context of method invocations of a class in commonly-used programs from a sampled population.Type: GrantFiled: March 29, 2018Date of Patent: June 2, 2020Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.Inventors: Jorge Banuelos, Shengyu Fu, Roshanak Zilouchian Moghaddam, Neelakantan Sundaresan, Siyu Yang, Ying Zhao