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: According to an aspect of an embodiment, operations include receiving first log data including first information associated with electronic trigger events from a trigger data source. The operations further include extracting trigger output parameters from a first sentence token of the received first log data and displaying a first set of trigger rules onto an electronic User Interface (UI) of a user device based on the extracted trigger output parameters. The operations further include receiving a first input comprising a selection of one or more trigger rules of the displayed first set of trigger rules and receiving a second input comprising a selection of computer-executable actions to be performed based on whether the first information satisfies the one or more trigger rules. The operations further include generating a computer-executable mashup code for execution on a runtime system based on the received first input and the received second input.

Abstract: A method may include obtaining one or more software-repository packages. A programming-language function may be extracted from the one or more software-repository packages. A curation resource associated with the programming-language function may be identified. The curation resource may include descriptive information related to the programming-language function. The method may include generating a code description corresponding to the programming-language function based on the curation resource. A function-comment pair that includes the programming-language function and the generated code description may be determined. A programming language corpus that includes the one or more software-repository packages may be generated and augmented by the function-comment pair. The method may include training a machine learning model using the programming language corpus.

Abstract: A method may include obtaining training code and extracting features from the training code. The extracted features of the training code may be mapped to natural language code vectors by a deep neural network. A natural language search query requesting source-code suggestions may be received, and the natural language search query may be mapped to a natural language search vector by the deep neural network. The method may include mapping the natural language search query to the natural language search vector in the same or a similar method as mapping the extracted features of the training code to natural language code vectors, and the natural language search vector may be compared to the natural language code vectors. Source code responsive to the natural language search query may be suggested based on the comparison between the natural language search vector and the natural language code vectors.

Abstract: A method to forecast a spike price of a commodity may include obtaining a request to perform a prediction of a spike in a price of a commodity. The method may also include obtaining additional information of the commodity based on the input. The method may also include determining, using a first machine learning algorithm, a probability of the spike based at least in part on the additional information. The method may include outputting the probability of the spike for visual representation on an electronic display in response to a determination that the probability of the spike is above a risk tolerance. The method may further include determining, using a second machine learning algorithm, a price prediction in response to a determination that the probability of the spike is below the risk tolerance, wherein determining the price prediction comprises outputting the price prediction for visual representation on the electronic display.

Abstract: According to an aspect of an embodiment, operations include deploying a trigger function on a computing system for detecting an electronic trigger event associated with a first Application Programming Interface (API) provider. The operations further include deploying a rule function for applying at least one trigger rule on event data and deploying an action function for generating at least one API call to a second API provider based on whether the event data satisfies the at least one trigger rule. The operations further include deploying a set of API gateways for enabling a workflow system to invoke at least one of: the deployed trigger function, the deployed rule function, and the deployed action function. The operations further include generating a workflow template of an API mashup based on information associated with the deployed functions and API gateways and posting the generated workflow template to the workflow system.

Abstract: A method may include obtaining multiple tasks. The multiple tasks may be configured to perform at least one function when executed. The method may also include obtaining an execution arrangement of a set of tasks of the multiple tasks. The execution arrangement may define when execution of functions of tasks of the set of tasks occur and may define data to be passed between at least two tasks of the set of tasks. The method may also include automatically generating a specification that includes a different object for each of the tasks of the set of tasks. The method may further include automatically generating executable code using the specification. The executable code may be configured to perform all the functions of all of the tasks of the set of tasks according to the execution arrangement based on a single user request.

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 obtaining metadata associated with first and second data sets that may be stored at remote computing devices. The method may also include constructing a first database based on the metadata, and performing a first processing task on the first data set. The first processing task may include obtaining raw data of the first data set, and importing the raw data of the first data set into a second database that may be configured as a relational database. The first processing task may also include generating relationships in the second database between entries of the first data set to facilitate anticipated responses to queries. The method may additionally include performing a second processing task different from the first processing task on the second data set, and after performing the first processing task, receiving a query that invokes the first data set, and responding to the query.

Abstract: A method may include receiving a selection of a task object for execution, the task object related to an application programming interface (API), determining whether the task object includes a placeholder value in the task object, and based on the task object including a placeholder value, producing an interface via which a user submits user input related to the placeholder value as described in the task object. The method may also include replacing the placeholder value with a new value that is based on the user input received via the interface, and generating an API call to the API in a target programming language, where the API call includes the new value. The method may also include deploying the generated API call on a target platform for execution, and obtaining results of the API call.

Abstract: A method may include obtaining a set of successful responses from one or more tasks, where the tasks include a specific implementation of an API call. The method may also include generating a first schema of a first response of the set of successful responses by extracting information from the first response, the first response responsive to a first task of the one or more tasks. The method may additionally include clustering the first schema with a second schema of a second response of the set of successful responses by applying a learning model to the first and second schemas, where the clustering is based on inputs to the first task and the first response, and creating an integrated task that includes the first task and a second task to which the second response is responsive based on the clustering.

Abstract: A method may include receiving a selection of an application programming interface (API) for which a task object is to be generated, and obtaining an array of parameters used in the task object, where each element in the array of parameters includes one or more of a constant value, a placeholder value, or combinations thereof, and the placeholder value includes programming to elicit user input to replace the placeholder value during execution of the task object. The method may also include combining the array of parameters with other information for the API for executing the task object into the task object, and storing the task object in a task object database.

Abstract: According to an aspect of an embodiment, operations include creating object-oriented software platform by performing a textual analysis of a documentation corpus associated with a set of APIs. The operations further include generating a set of API call objects for an API endpoint of a first API of the set of APIs and constructing a set of natural language descriptors corresponding to the set of API call objects based on the textual analysis. The operations further include generating a set of business objects. Each business object encapsulates conditions applicable on a set of input/output parameters associated with a corresponding API call object of the set of API call objects. The operations further include constructing a software package that encapsulates the set of API call objects, the set of natural language descriptors, and the set of business objects.

Abstract: According to an aspect of an embodiment, operations include receiving a first input corresponding to a selection of one or more Application Programming Interface (API)-based trigger options associated with one or more electronic trigger events. The operations further include receiving a second input corresponding to a selection of one or more trigger rules which are applicable on event data associated with the one or more electronic trigger events and receiving a third input corresponding to a selection of one or more API-based actions. The operations further include constructing an API mashup template based on the first input, the second input, and the third input and generating an API mashup code based on the constructed API mashup template. The API mashup code is configured to be computer-executable on a runtime system.

Abstract: 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.

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 obtaining a specification for a resource that includes a description of how the resource operates and interacts with another resource. The method may include generating a diagram that includes each word in the description in an order of word types. The method may include generating a second specification that includes the description and the diagram. The method may include generating tasks for the resource. Each task may include a description based on the description of the resource and diagram. The method may include generating hierarchy descriptions for each task based on the task descriptions. The method may include creating a model for each task that includes the hierarchy descriptions for the corresponding task. The hierarchy descriptions may be arranged according to the diagram in the second specification. The method may include presenting one of the hierarchy descriptions for the tasks according to the models.

Abstract: An electronic device receives a first report from a first communication device. The first report is related to a first incident associated with a first vehicle corresponding to the first communication device. The electronic device further receives a second report from a set of second communication devices that lie within a first zonal range of the first vehicle. The electronic device further transmits the first report and the second report to a distributed ledger communicatively coupled to the electronic device. The electronic device further generates consensus information to validate the first incident, based on a smart contract, the received first report, and the received second report. The electronic device further generates a conclusive incident report related to the first incident based on the generated consensus information and generates regulation information based on one of the conclusive incident report, the first report, or the second report, stored on the distributed ledger.

Abstract: According to an aspect of an embodiment, operations include receiving first log data including first information associated with electronic trigger events from a trigger data source. The operations further include extracting trigger output parameters from a first sentence token of the received first log data and displaying a first set of trigger rules onto an electronic User Interface (UI) of a user device based on the extracted trigger output parameters. The operations further include receiving a first input comprising a selection of one or more trigger rules of the displayed first set of trigger rules and receiving a second input comprising a selection of computer-executable actions to be performed based on whether the first information satisfies the one or more trigger rules. The operations further include generating a computer-executable mashup code for execution on a runtime system based on the received first input and the received second input.

Abstract: 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.