Fujitsu Corporation

Junhee Park

Wei-Peng Chen

Mehdi Bahrami

Lei Liu

Samir Parikh

Athar Sefid

According to an aspect of an embodiment, operations may include receiving a first input corresponding to a selection of a combinatorial optimization problem and receiving a set of datapoints as input. The operations may further include generating a first quadratic unconstrained binary optimization (QUBO) formulation based on an objective function for the combinatorial optimization problem and the received set of datapoints. The operations may further include selecting a first privacy setting and encoding the first QUBO formulation based on the selected privacy setting to generate a second QUBO formulation. The operations may further include submitting the generated second QUBO formulation to an optimization solver machine and receiving a first solution of the second QUBO formulation. The operations may further include decoding the first solution to produce a second solution and publishing an output of the combinatorial optimization problem on a user device based on the second solution.

Data privacy protection on cloud-based optimization systems

A method for parameter value resolution, includes extracting, from an application programming interface (API) repository, metadata information associated with an API parameter set of at least one API and generating parameter clusters associated with the API parameter set based on the extracted metadata information. Each parameter cluster may include word tokens associated with at least one API parameter of the API parameters set. The method furthers includes determining a representative term for each parameter cluster and based on the determined representative term, extracting sample parameter values from a knowledge base server. The method further includes generating test API endpoints associated with the at least one API based on the extracted sample parameter values, validating the extracted sample parameter values by executing the generated test API endpoints, and publishing, based on the validation, validation result as an indicator for suitability of the at least one API for a software application.

Automatic parameter value resolution for API evaluation

A method may include iterating over multiple application programming interfaces (APIs) to extract an authentication type for the multiple APIs. The method may also include, from a central repository, automatically registering a new user for access to a given API of the multiple APIs. The method may additionally include obtaining authentication data based on an authentication type for the given API, and storing the authentication data. The method may also include accessing the given API using the stored authentication data.

Automated API access using machine learning

A bot network system may be provided. A system may include a plurality of bot agents, wherein at least one bot agent of the plurality of bot agents is configured to receive a request from a user in natural language. The system may further include a plurality of digital resources including one or more of a software program, a service, a web service and a dataset. Each digital resource of the plurality of digital resources may be configured to communicate with a dedicated bot agent of the plurality of bot agents. Also, each bot agent may be configured to interact with its associated digital resource via an application programming interface (API) of the associated digital resource and translate between the natural language and a language of the associated digital resource.

Bot networks

A system for providing access to at least one of an application programming interface (API) and an Internet of Thing (IoT) device is provided. A system may include one or more processors configured to establish a connection between at least one user device and the at least one of the API and the IoT device. The one or more processors may further be configured to allocate resources for one or more services associated with the at least one of the API and the IoT device and provide the one or more services associated with the at least one of the API and the IoT device to the at least one user device. The one or more processors may also be configured to monitor at least one operational parameter associated with the one or more services provided to the at least one user device.

Providing access to application program interfaces and Internet of Thing devices

A method of evaluating and selecting an application programming interface (API). The method includes accessing source-code in computer programming languages from an API repository. The method includes training a machine learning classifier using generalized, extracted API-calls to learn variations of API-call programming language syntaxes through usage of the API-call programming language syntaxes in the source-code. The method includes building API-call models suitable for the computer programming languages and building a parameter relations graph based on backtracking data-flow from API-calls that are extracted from public project repositories based on the API-call model. The method includes retrieving sample parameter values from the parameter relations graph. The method includes implementing the sample parameter values in an evaluation interface to evaluate functionality of an API in a software application.

Automated API evaluation based on API parameter resolution

A method of test-assisted application programming interface (API) learning includes generating a machine-readable API specification (API spec). The API spec is based on application of machine learning and regular expression processes to an API documentation. The method includes comparing the API spec to other API specifications. Based on the comparison, the method includes identifying a set of similar API specifications. The method includes generating API test inputs based on the set of similar API specifications and the API spec. The method includes calling a native API system using a first API test input of the API test inputs. The method includes receiving a response indicative of whether the first API test input successfully interacts with the native API system. Based on the response, the method includes generating a feedback indicative of an alteration to the API test inputs or to the machine learning or the regular expression processes.

Test-assisted application programming interface (API) learning

A method for automated application programming interface (API) validation includes extracting API information from an API repository. The API information is used to generate a test case for the automated API validation. The API information may include a parameter placeholder, parameter information related to a parameter of an API endpoint, an API endpoint of the API, an endpoint description, a description of the API, a description of the parameter, response information, an authentication requirement information, and an API name. The method includes resolving the parameter of the API endpoint. The method includes communicating to a native API system a request using the sample parameter value for the parameter. The method includes comparing a response from the native API system with the response information to validate the API. The method includes verifying integrity of a software application implementing the API endpoint for use with a native software application.

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San Jose, CA, United States of America

Junhee Park