International Business Machines Corporation

General Electric Company

Joji P George

Sandeep Korrapati

Kousik Dan

Nitish Jindal

Amarnath Thulabanthula

Ramkumar Saptharishi

Aavishkar Bharara

Madhuri Soma

A method, system, and computer program product for parallel regression to bypass regression process interruption are provided. The method identifies an RTL design. The RTL design is converted into a control data flow graph having a plurality of nodes and a plurality of arcs. A mapping table is generated with a machine learning database from a set of test cases based on the control data flow graph. The method selectively calls one or more test cases of the set of test cases to merge a branch to a main repository to bypass a portion of a regression process.

Collateral correlated regression in version control repository

A method, system, and computer program product are described for use with graph-based verification of a circuit design. The method comprises performing, using a graph referencing the circuit design, a predefined set of one or more test sequences. The method further comprises determining that a first coverage point of the graph is not fully hit by the predefined set, and back-traversing the graph from a second coverage point of the graph that is partially hit or fully hit by the predefined set. The method further comprises generating, based on back-traversing the graph, a prospective test sequence to fully hit the first coverage point.

Functional coverage enhancement in portable stimulus graphs

In one embodiment, a method of deriving probability of a medical condition is provided. The method comprises obtaining a first medical data corresponding to a first medical subject from at least one medical information system, obtaining a second medical data corresponding to the first medical subject from an electronic data system, selecting at least one second medical subject with a second medical data substantially same as the second medical data of the first medical subject, obtaining medical condition data for the second medical subject, wherein the medical condition data includes data corresponding to a medical condition, correlating the first medical data with the medical condition data and deriving probability of the medical condition in the first medical subject based upon the first medical data and the medical condition data.

System and method for predicting medical condition

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Bengaluru, India

Joji P George