Sepaton, Inc.

Emc IP Holding Company LLC

Hitachi Data Systems Corporation

David Biernacki

Carey Jay McMaster

Timmie G Reiter

Ronald Ray Trimble

Jon Christopher Kennedy

Stefan Merrill King

Mark Adam Arakelian

Tamilarasan Janakiraman

Debra Robitaille

Venkat Reddy

Belagapu Kumar

Suhas K B

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.

Method and apparatus for predicting errors in to-be-developed software updates

A system configured to compute match potential between first data and second data is provided. The system includes data storage storing the first data and the second data, and at least one processor coupled to the data storage. The at least one processor is configured to identify a first sequence of fingerprints characterizing a first plurality of sections of the first data, the first sequence being ordered according to an order of the first plurality of sections within the first data; identify a second sequence of fingerprints comprising fingerprints that match fingerprints within the first sequence, the second sequence of fingerprints characterizing a second plurality of sections of the second data, the second sequence being ordered according to an order of the second plurality of sections within the second data; quantify a similarity between the first sequence and the second sequence; and adjust the match potential based on the similarity.

Systems and methods of locating redundant data using patterns of matching fingerprints

Described are computer-based methods and apparatuses, including computer program products, for removing redundant data from a storage system. In one example, a data delineation process delineates data targeted for de-duplication into regions using a plurality of markers. The de-duplication system determines which of these regions should be subject to further de-duplication processing by comparing metadata representing the regions to metadata representing regions of a reference data set. The de-duplication system identifies an area of data that incorporates the regions that should be subject to further de-duplication processing and de-duplicates this area with reference to a corresponding area within the reference data set.

System and method for summarizing data

System and method for data driven de-duplication

System and method for identifying locations within data

System and method for providing data driven de-duplication services

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Woonsocket, RI, United States of America

David Biernacki