Toyota Jidosha Kabushiki Kaisha

Sumanth Dathathri

Nikos Arechiga Gonzalez

Shashank Jivan Vernekar

Shinichi Shiraishi

The technology can advantageously improve the speed of software verification using an approximation of a datastore storing a multiplicity of indexed data. An example method may determine data point(s), suspected to violate a specification of a software program being verified, using approximation(s) of datastore(s) of the software program. Data ranges reflecting subset(s) of data from the approximation(s) that contain the data points may be determined and data entries stored in the datastore(s) may be searched for the data point(s) suspected to violate the specification. The data entries lie within the data range(s) and the method determines whether the data point(s) are in violation of the specification based on the searching.

Falsification of software program with datastore(s)

The technology can compute an approximation of a datastore storing a multiplicity of indexed data. An example method can a template including programming logic that, when executed, calculates output(s) based on input(s) and undetermined parameter(s). The undetermined parameter(s) are input into a machine learning framework. Data entries reflecting one or more inputs are retrieved from a datastore and input into the machine learning framework, which determines value(s) for the undetermined parameter(s), respectively (making them determined parameters). The example method generates an approximation of the datastore using the determined parameter(s) and the input(s).

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Pasadena, CA, United States of America

Sumanth Dathathri