Hitachi, Ltd.

Hideto Ogawa

Naoto Sato

Hironobu Kuruma

Tomoyuki Myojin

Makoto Ishikawa

Shinji Itoh

Yuichiroh Nakagawa

To efficiently verify and improve a robustness of a learning model for supervised machine learning. A data-creation assistance apparatusincludes: a storage deviceconfigured to store a neural network modeland test data; and a computing deviceconfigured to specify an uncertainty of an inference result acquired by the neural network model; acquire gradient information of the test databy a back propagation process using the uncertainty as a loss; apply various minute changes to the test datato generate a plurality of minutely changed test data, and calculate deviations between each of the plurality of pieces minutely changed test data and the test data; and specify, based on the uncertainty information, the gradient information, and the deviations, a minute change that increases or decreases the uncertainty.

Data-creation assistance apparatus and data-creation assistance method

To make validity of a prediction model created by machine learning be able to be verified with appropriate accuracy and efficiency. A software test apparatus includes a storage device configured to store a prediction model, and an arithmetic device. The arithmetic device is configured to accept inputs of a precondition, a constraint condition, and an approximation threshold value, convert the prediction model into a logical expression, analyze an approximation range based on the approximation threshold value with respect to the logical expression to simplify the logical expression, generate an inspection expression by combining the simplified logical expression with the precondition and negation of the constraint condition, search for, as a counterexample, a value satisfying the inspection expression, input the value to the prediction model to evaluate inspection accuracy when the counterexample exists, and output a result of the evaluation.

Software test apparatus and software test method

Software test equipment including a data conversion unit receives test input data to convert the test input data into software input data to be provided to software and model input data, a software execution unit receives the software input data, executes the test target software based on the software input data, and outputs an execution result, a model execution unit receives a reference model for the software to generate a model allowable output value range of the execution result obtained by executing the software, based on the model input data and the reference model, a difference analysis unit generates difference information based on the execution result output by the software execution unit and the model allowable output value range generated by the model execution unit, and an evaluation unit receives evaluation criteria and evaluates the behavior of the software based on the difference information and the evaluation criteria.

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Tokyo, Japan

Hideto Ogawa