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.

Abstract: A method may include receiving user input indicative of a style configuration for API formats. The method may include generating a style file that includes data representative of the style configurations. The method may include receiving an API documentation that includes API objects and determining an API format associated with the API documentation. Responsive to the API format associated with the API documentation satisfying a similarity threshold, the method may include generating a temporary file that includes data representative of the API documentation. The method may include generating a display file that includes data based on the style and temporary files. The display file may be generated to include a portion of the style and temporary files. The method may include directing display of a GUI that displays the API documentation based on the display file. Each API object may be displayed to include formatting according to the style configuration.

Abstract: 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.

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: A method may include obtaining input data for an application programming interface (API), and encrypting the input data for the API using a public key of a provider of the API. The method may also include transmitting, to an API management server, an API request that invokes the API, where the API request includes an API call for the API and the encrypted input data. The API request may be in a format such that the API management server is capable of performing API management services based on the API call but unable to decrypt the encrypted input data with the public key.

Abstract: A method may include accessing, by a processor, a first server associated with a first provider of at least one application programming interface (API); automatically selecting, by the processor, the at least one API provided by the first provider; constructing a list of features associated with the selected at least one API; parsing, by the processor, a first HyperText Transfer Protocol (HTML) page associated with the selected at least one API; automatically simulating, by the processor, at least one user interaction with the first HTML page; extracting, by the processor, API object information based on: a) constructing the list of features, b) parsing the first HTML page, and c) automatically simulating the at least one user interaction with the first HTML page; and constructing, by the processor, a machine-readable API specification based on the extracted API object information.

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 context data describing a context of a user of an electronic device and transmitting the context data to a back-end device. The method may also include receiving instructions from the back-end device, the instructions directing the electronic device to dynamically generate a single user interface including interactive content from a plurality of related resources, the plurality of related resources determined by the back-end device to be related to each other and to the context of the user, features of the interactive dynamic content extracted by the back-end device. The method may additionally include dynamically generating the single user interface, and displaying the single user interface to the user of the electronic device.

Abstract: According to an aspect of an embodiment, operations may include displaying a electronic user interface that includes a plurality of user-selectable options corresponding to taxonomy information for a plurality of optimization problems. The operations may further include receiving a first user input selecting a first template for a specific optimization problem of the plurality of optimization problems. The first user input may include a selection of at least one user-selectable option of the plurality of user-selectable options. The operations may further include receiving a second user input via the selected first template for the specific optimization problem and providing a call to the optimization solver machine to generate a solution for the specific optimization problem based on the received second user input. The second user input may include input data for a plurality of parameters of the specific optimization problem, specified in the selected first template.

Abstract: A method may include providing access to a first application programming interface (API) provided by a first party and a second API provided by a second party. The method may also include collecting a first set of API data sources related to the first API and a second set of API data sources related to the second API. The method may additionally include using a deep learning model to predict a privacy trustworthiness level for the first API and the second API, and disabling access to the first API based on the privacy trustworthiness level of the first API being below a threshold level.

Abstract: A method may include obtaining first API documentation associated with a first API and second API documentation associated with a second API. The method may include mining the first API documentation to extract a first plurality of semantic triples and mining the second API documentation to extract a second plurality of semantic triples. The method may include selecting a first subset of semantic triples from the first plurality of semantic triples and a second subset of semantic triples from the second plurality of semantic triples. The method may include generating a first ontology based on the first API documentation and the first subset of semantic triples. The method may include generating a second ontology. The method may include correlating the first ontology with the second ontology. The method may include generating a knowledge graph of the first API and the second API.

Abstract: A method includes extracting, from a web application programming interface (API) repository, first information associated with a plurality of web APIs and constructing an initial dataset associated with the plurality of web APIs by performing a first set of information processing operations on the extracted first information. The method further includes constructing a training dataset by performing a second set of information processing operations on the constructed initial dataset and obtaining a Machine Learning (ML) model based on the constructed training dataset. The method further includes receiving an input natural language query via an electronic user interface (UI) and providing the received input natural language query to the obtained ML model. The method further includes outputting a set of recommendation results based on the provided input natural language query to the obtained ML model. Each recommendation result includes a specific API name and a specific endpoint.

Abstract: A method includes extracting, from a labelled repository, a plurality of true endpoint descriptions associated with a plurality of web APIs and retrieving a documentation corpus associated with the plurality of web APIs. The method further includes determining a plurality of false endpoint descriptions associated with the plurality of web APIs based on the retrieved documentation corpus and the extracted plurality of true endpoint descriptions. The method further includes extracting feature information associated with the plurality of web APIs, generating a training dataset based on the extracted feature information, and obtaining a ML model based on the generated training dataset. The method further includes providing a set of inputs to the ML model and classifying the provided set of inputs as one of a true description or a false description associated with an endpoint based on a prediction result of the ML model for the provided set of inputs.

Abstract: A method may include receiving user input indicative of a style configuration for API formats. The method may include generating a style file that includes data representative of the style configurations. The method may include receiving an API documentation that includes API objects and determining an API format associated with the API documentation. Responsive to the API format associated with the API documentation satisfying a similarity threshold, the method may include generating a temporary file that includes data representative of the API documentation. The method may include generating a display file that includes data based on the style and temporary files. The display file may be generated to include a portion of the style and temporary files. The method may include directing display of a GUI that displays the API documentation based on the display file. Each API object may be displayed to include formatting according to the style configuration.

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 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 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 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.