Patents by Inventor Suhas K B

Suhas K B 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).

  • Method and apparatus for predicting errors in to-be-developed software updates
    Patent number: 11055178
    Abstract: A method of providing an error occurrence estimate for a proposed software update, before the proposed software update is created, includes training a learning process to cause the learning process to learn a correlation between the complexity of the previous software updates and the error occurrences of the previous software updates. The complexity information may include the number of lines of code and the number of check-in operations that occurred in connection with creation of the previous software updates. The trained learning process is then provided with expected complexity information of a proposed software update, and used to generate an error estimate including the number of errors that are likely to occur, the severity of the errors that are likely to occur, and the amount of software developer time that should be expected to be incurred to correct the errors.
    Type: Grant
    Filed: August 19, 2019
    Date of Patent: July 6, 2021
    Assignee: EMC IP Holding Company LLC
    Inventors: David Biernacki, Debra Robitaille, Mark Adam Arakelian, Venkat Reddy, Belagapu Kumar, Suhas K B, Tamilarasan Janakiraman
  • Method and Apparatus for Predicting Errors in To-Be-Developed Software Updates
    Publication number: 20210055995
    Abstract: A method of providing an error occurrence estimate for a proposed software update, before the proposed software update is created, includes training a learning process to cause the learning process to learn a correlation between the complexity of the previous software updates and the error occurrences of the previous software updates. The complexity information may include the number of lines of code and the number of check-in operations that occurred in connection with creation of the previous software updates. The trained learning process is then provided with expected complexity information of a proposed software update, and used to generate an error estimate including the number of errors that are likely to occur, the severity of the errors that are likely to occur, and the amount of software developer time that should be expected to be incurred to correct the errors.
    Type: Application
    Filed: August 19, 2019
    Publication date: February 25, 2021
    Inventors: David Biernacki, Debra Robitaille, Mark Adam Arakelian, Venkat Reddy, Belagapu Kumar, Suhas K B, Tamilarasan Janakiraman