Abstract: A method may include collecting a plurality of computer-readable source code from an application programming interface (API) repository. The method may also include performing API call code slicing on the computer-readable source code. The method may also include extracting an API host and an endpoint for each of the API call codes identified in the API call code slicing. The method may also include compiling an API mashup database based on the extracted API host and endpoints. Furthermore, the method may also include providing a recommended API mashup of the API mashup database in a user interface.

Abstract: A method may include extracting information regarding a software project based on source code of the software project, where the information includes a description of the source code and computer-readable source code. The method may also include identifying at least two application programming interface (API) calls within the information, and extracting metadata regarding at least one of the APIs from the information. The method may additionally include, using the metadata, analyzing the computer-readable source code to determine a correlation between the two API calls. The method may also include, based on the correlation and using a first of the two API calls, generating computer source code that combines the first API call and an additional API call in an API mashup.

Abstract: A method of automated ticket resolution comprises training and testing feature-specific classifier models using ticket database records. The feature-specific classifier models include machine-learning-based classification models related to features of a ticket system. The method includes training and testing feature-specific solution models using resolved ticket solution database records. The feature-specific classifier models include machine-learning-based solution models related to the features. The method includes receiving a ticket inquiry including a ticket indicative of an issue related to the features, generating a problem statement representative of the issue using the tested classifier models, and communicating the problem statement to the tested solution models. The method includes predicting a solution to the problem statement by using the tested solution models. The solution includes directions to resolve the ticket.

Abstract: A generic application program interface (API) may learn heterogeneous APIs based on their API documentations that understands functionalities, parameters, operations, accessibility and outputs of APIs. The generic API collects API documentation from different sources based on a target human language by crawling a network such as the Internet. The generic API reads the documents of an API to recognize features of an API including type of services, concepts, protocols, accessibility methods, security methods, functionalities, outputs and base operations. Each given instruction for different APIs is mapped to generic API features. A programmer or end-user have access to multiple and heterogeneous APIs based on the generic API. The generic API provides a unified interface that allows a programmer or end-user to interact with different target APIs through the generic API. By raising a request to generic API a native API code is generated, executed and returns to the programmer or end-user.

Abstract: 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.

Abstract: 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.

Abstract: A method may include receiving first and second application programming interface (API) specifications. The method may include comparing first and second API Specification according to a set of criteria. Responsive to the first and second criteria being identical, the method may include identifying first and second objects. Responsive to the first and second criteria being identical, the method may include classifying a type of the first and second objects and associating one or more parameters in the first and second objects with a consolidated object. The one or more parameters in the first and second objects may be associated with the consolidated object based on the type of the first and second object. The method may include generating a consolidated specification that includes the consolidated object and a description of the one or more parameters of the first and second objects that are associated with the consolidated object in a single API specification.

Abstract: A system may include a memory, a communication interface, and a processor operatively coupled to the communication interface. The processor may be configured to generate a set of classification models. The classification models may include one or more sentences associated with application program interface (API) documentations. The processor may also be configured to access one or more content sources for content. Additionally, the processor may be configured to determine whether one or more sentences in the content is the same or substantially similar to one or more sentences included in the classification models. In response to determining one or more sentences in the content is the same or substantially similar, the processor may be configured to collect the content as API documentation. Additionally, the processor may be configured to categorize different type of API documentations, such as REST API documentations, JAVA API documentations, etc.

Abstract: A system configured for curtailment event implementation based on uncertainty of energy demand flexibility of sites includes a processor and a non-transitory computer-readable medium communicatively coupled to the processor and having encoded therein programming code executable by the processor to perform operations. The operations may include receiving energy usage data from meters at sites and uncertainties of energy demand flexibility for each site. The uncertainties of energy demand flexibility of each site may be zero, may fit a single probability distribution, may fit a set of probability distributions, or may belong to a set of demand flexibilities. The operations may include clustering the sites based on the uncertainties. The operations may include solving group-specific formulations for groups. The operations may include identifying sites to include in the DR event using the solved-for group-specific parameters.

Abstract: According to an aspect of an embodiment, a method may include obtaining a first document associated with a particular API and a first Uniform Resource Identifier (URI), a second document associated with the particular API and a second URI, and an API name of the particular API. The method may further include identifying a URI prefix included in the first URI and the second URI. The method may further include determining a correlation between the URI prefix and the API name. The method may further include determining that the URI prefix is associated with the particular API based on the correlation. The method may further include associating the first document and the second document with the particular API based on the first URI and the second URI including the URI prefix and based on determining that the URI prefix is associated with the particular API.

Abstract: A method may include obtaining an application programming interface (API) document, such as an API user guide, API reference, or other API documentation, that includes information regarding an API resource, and extracting information from the API document, where the information including at least one function and at least one table that includes an attribute associated with the API resource. The method may also include extracting a description for each of the at least one functions from the API document, and comparing the extracted at least one table to a model of attributes to correlate extracted attributes with extracted functions. The method may additionally include compiling a machine-readable API specification for the API resource described in the API document based on the correlation between extracted attributes and the extracted functions.

Abstract: A JavaScript Object Notation (JSON) data validation method may include generating a description schema for defining JSON data using one or more JSON constructs. The method may further include converting the description schema to an Extensible Markup Language (XML) schema. The method may also include converting the JSON data to XML data, and validating the JSON data based on the XML Schema and the XML data.

Abstract: A method of machine learning may include receiving an API documentation including an endpoint and corresponding metadata. The method may include receiving a verified API specification including a verified endpoint. The method may include comparing the endpoint to the verified endpoint. The method may include classifying the metadata. The metadata may be classified as a positive item responsive to the endpoint being substantially the same as the verified endpoint. The metadata may be classified as a negative item responsive to the endpoint not being substantially the same as the verified endpoint. The method may include generating a training dataset, which may include the metadata classified as a positive item or a negative item. The method may include generating a metadata model using a machine learning algorithm and the training dataset. The metadata model may be configured to be used to determine whether an unknown endpoint is a positive endpoint.

Abstract: 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.

Abstract: 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.

Abstract: A gateway includes a communication interface and a processor. The processor is configured to receive, via the communication interface, a graphical representation of a device to be connected to the gateway, the graphical representation being in an electronic format. The processor is further configured to identify the device using the graphical representation to locate a record for the device in an electronic data storage. The processor is configured to search, connect and interact with a variety of the Internet of Things (IoT) devices or services. The processor is also configured to record and/or monitor all connected devices. The processor is also configured to provide a recommendation for the possible connection via a graphical user interface based on monitored/recorded patterns, which are permitted. The processor is configured to learn the monitored/recorded patterns based machine learning methods and trigger some actions with users' permissions or recommend a set of services to users.

Abstract: A method may include receiving a request to obtain a data file from a nearest remote computing system in a blockchain network and requesting a transaction history of the data file from the blockchain network. The method may also include determining the nearest remote computing system to the requesting computing system based on the transaction history, where the nearest remote computing system stores a copy of the transaction history and the data file and determining the data file stored on the nearest remote computing system is the same as the requested data file by comparing a first hash value associated with the requested data file to a second hash value associated with the data file stored on the nearest remote computing system. The method may also include receiving the requested data file from the nearest remote computing system; and sending the requested data file to the requesting computing system.

Abstract: The present disclosure includes a method of routing packets. The method may include receiving, at a network device, an interest packet that requests content from an ICN network, where the network device is configured to communicate in the ICN network via one or more paths in an address-routing network. In the method, the ICN network may be overlaid on one or more network devices communicating in the address-routing network. The method may additionally include determining, between a first and second alternative path, a routing path of where to route the interest packet based on network costs associated with obtaining the content along each of the first and the second alternative paths through the address-routing network. The method may also include sending the interest packet along the routing path.

Abstract: Technologies are described to control network congestion in packet-based networks. In some examples, a method may include receiving an Interest packet requesting content, and returning the content from a local data store if the content is in the local data store. The method may also include determining whether a previous request has been made for the requested content if the content is not in the local data store, and creating a record of the Interest packet and discarding the Interest packet if a previous request has been made. The method may further include determining whether a local IP routing table includes an entry that matches a destination IP address specified by the Interest packet if a previous request has not been made, and forwarding the Interest packet if the destination IP address is in the local IP routing table.

Abstract: A method of determining application program interface (API) mashups is provided. The method may include identifying an endpoint description for a first API, and determining whether the endpoint description includes at least one of input description data and output description data. Further, the method may include, in response to the endpoint description including the input description data, determining at least one possible API mashup including the first API and a second API based on a comparison of the input description data and an output description of the second API. Moreover, the method may include, in response to the endpoint description including the output description data, determining one or more possible API mashups including the first API and the second API based on a comparison of the output description data and an input description of the second API.