Abstract: A device may receive test scripts that include first information identifying first elements of user interfaces or second information identifying test steps. The test scripts may be written in first text or first program code. The device may process the first text or the first program code of the test scripts. The device may identify the first elements on the user interfaces. The first elements may be identified without using second program code associated with the user interfaces. The first elements may be identified based on a type of the first elements, second text associated with the first elements, or a relationship between the first elements and second elements. The device may identify positions for the first elements. The positions may permit the device to interact with the first elements to perform the test steps. The device may perform the test steps to test the user interfaces.

Abstract: A device may receive information regarding a software environment and/or one or more modules associated with the software environment; receive information regarding one or more compliance requirements; determine one or more smart contracts based on the information regarding the one or more compliance requirements and the information regarding the software environment and/or the one or more modules associated with the software environment; obtain data regarding the software environment and/or the one or more modules; determine, using the one or more smart contracts, whether the data satisfies the one or more compliance requirements; and add at least part of a block, to a blockchain, to indicate whether the data satisfies the one or more compliance requirements, for realization of a reward or penalty associated with the one or more compliance requirements.

Abstract: According to an example, partitioning based migration of systems to container and micro-service based-platforms may include determining, based on an analysis of source code for an application that is to be partitioned, an entity model corresponding to the application, identifying resources associated with the application, and determining a mapping of the identified resources to entities of the entity model. Further, partitioning based migration of systems to container and micro-service based-platforms may include identifying dependencies for each of the mapped resources, generating dependency and control flow metrics for the application, generating affinity values between the mapped resources, generating a resource affinity graph, determining an affinity score between each of the mapped resources, and generating resource clusters that correspond to partitions of the application.

Abstract: A device may receive information identifying a first set of instructions. The first set of instructions may identify an action to perform to test a first program. The device may identify a second set of instructions, related to testing a second program, that can be used in association with the first set of instructions. The first test may be similar to the second test. The device may identify multiple steps, of the first set of instructions, that can be combined to form a third set of instructions. The third set of instructions may be used to test the first program or a third program. The device may generate program code in a first programming language to perform the action. The first programming language may be different than a second programming language used to write the first set of instructions. The device may perform the action.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reconciliation data.

Abstract: A device may obtain test case information for a set of test cases. The test case information may include test case description information, test case environment information, and/or test case defect information. The device may determine a set of field-level similarity scores by using a set of similarity analysis techniques to analyze a set of test case field groups associated with the test case information. The device may determine a set of overall similarity scores for a set of test case groups by using a machine learning technique to analyze the set of field-level similarity scores. The device may update a data structure that stores the test case information to establish one or more associations between the test case information and the set of overall similarity scores. The device may process a request from a user device using information included in the updated data structure.

Abstract: A device may obtain a document. The device may identify a skip value for the document. The skip value may relate to a quantity of words or a quantity of characters that are to be skipped in an n-gram. The device may determine one or more skip n-grams using the skip value for the document. A skip n-gram, of the one or more skip n-grams, may include a sequence of one or more words or one or more characters with a set of occurrences in the document. The sequence of one or more words or one or more characters may include a skip value quantity of words or characters within the sequence. The device may extract one or more terms from the document based on the one or more skip n-grams. The device may provide information identifying the one or more terms.

Abstract: A device may receive, from a user device, a request to verify a machine learning (ML) application using a metamorphic testing procedure. The device may determine a type of ML process used by the ML application, and may select one or more metamorphic relations (MRs), to be used for performing the metamorphic testing procedure, based on the type of ML process. The device may receive test data to be used to test the ML application, wherein the test data is based on the one or more MRs, and may perform, by using the one or more MRs and the test data, the metamorphic testing procedure to verify one or more aspects of the ML application.

Abstract: A device may receive a text, from a text source, in association with a request to generate an ontology for the text. The device may generate a set of word vectors from a list of terms determined from the text. The device may determine a quantity of term clusters to be generated to form the ontology based on the set of word vectors. The device may generate term clusters based on the quantity of term clusters, attributes, and/or non-hierarchical relationships. The term clusters may be associated with concepts of the ontology. The device may provide the term clusters for display via a user interface associated with a device.

Abstract: Methods and systems including computer programs encoded on a computer storage medium, for interactive content recommendation. In one aspect, a method includes receiving a request for content by a user, determining a user intent based on the received request, providing to the user a first attribute responsive to the user intent, receiving a first attribute value responsive to the first attribute, providing a second attribute, and receiving a second attribute value responsive to the second attribute. A particular content vector including a first content attribute and a second content attribute for a particular content item is identified where the first content attribute and the second content attribute sufficiently match the first attribute value and the second attribute value. The particular content item is provided as a suggested content item, and, responsive to a user selection of the particular content item, provided for presentation on the user device.

Abstract: A method may include receiving a plurality of samples that include textual content. The method may include extracting unit values, corresponding to structural units, from the plurality of samples. The structural units may identify characteristics of the plurality of samples to be used to identify pattern information. The pattern information may identify unit values that are shared between at least two samples of the plurality of samples. The method may include generating one or more structural representations based on the unit values. The one or more structural representations may identify the pattern information. The method may include generating one or more additional samples based on the one or more structural representations. The one or more additional samples may include at least one of the unit values, and may be generated based on the pattern information. The method may include outputting the one or more additional samples.

Abstract: A device may obtain a test script document. The device may process the test script document to perform term extraction using one or more term extraction techniques to identify a set of terms of the test script document. The one or more term extraction techniques may include a skip n-gram term extraction technique. One or more terms, of the set of terms, may be located within an n-gram of the test script document. The device may process the test script document to perform hierarchy formation for results of performing term extraction. A relationship between a set of terms, of the set of terms, may be identified using hierarchy formation. The device may generate a functional diagram of the test script document based on the results of performing term extraction and results of performing hierarchy formation. The device may provide information identifying the functional diagram.

Abstract: According to an example, partitioning based migration of systems to container and micro-service based-platforms may include determining, based on an analysis of source code for an application that is to be partitioned, an entity model corresponding to the application, identifying resources associated with the application, and determining a mapping of the identified resources to entities of the entity model. Further, partitioning based migration of systems to container and micro-service based-platforms may include identifying dependencies for each of the mapped resources, generating dependency and control flow metrics for the application, generating affinity values between the mapped resources, generating a resource affinity graph, determining an affinity score between each of the mapped resources, and generating resource clusters that correspond to partitions of the application.

Abstract: In some examples, natural language eminence based robotic agent control may include ascertaining, by a robotic agent, an image of an object or an environment, and ascertaining a plurality of natural language insights for the image. For each insight of the plurality of insights, an eminence score may be generated, and each insight of the plurality of insights may be ranked according to the eminence scores. An operation associated with the robotic agent, the object, or the environment may be controlled by the robotic agent and based on a highest ranked insight.

Abstract: In some examples, natural language unification based robotic agent control may include ascertaining, by a robotic agent, an image of an object or an environment, and ascertaining a plurality of natural language insights for the image. A semantic relatedness may be determined between each insight of the plurality of insights, and a semantic relatedness graph may be generated for the plurality of insights. For each insight of the plurality of insights, at least one central concept may be identified. Based on the semantic relatedness graph and the identified at least one central concept, the plurality of insights may be clustered to generate at least one insights cluster. For insights included in the least one insights cluster, a unified insight may be generated. Further, an operation associated with the robotic agent, the object, or the environment may be controlled by the robotic agent and based on the unified insight.

Abstract: A device may receive a set of first samples of textual content. A device may identify a set of clusters of first samples of the set of first samples. A device may identify a pattern of occurrence based on the set of clusters. The pattern of occurrence to identify two or more clusters, of the set of clusters, based on an order in which first samples associated with the two or more clusters were generated or received. A device may receive one or more second samples of textual content. A device may determine that the one or more second samples are semantically similar to one or more corresponding clusters associated with the pattern of occurrence. A device may identify a predicted sample based on the pattern of occurrence and the one or more corresponding clusters. A device may perform an action based on identifying the predicted sample.

Abstract: A device may obtain text to be processed to identify a trend associated with a topic included in the text. The text may include a plurality of text sections, associated with the topic, that may be associated with a plurality of temporal intervals. The device may determine a respective context for the topic in each of the plurality of text sections. The device may calculate a first specificity score based on the respective context for the topic for one or more text sections associated with the first temporal interval. The device may calculate a second specificity score based on the respective context for the topic for one or more text sections associated with the second temporal interval. The device may identify a trend associated with the topic based on the first specificity score and the second specificity score, and may provide information that identifies the trend.

Abstract: A device may obtain a document. The device may identify a skip value for the document. The skip value may relate to a quantity of words or a quantity of characters that are to be skipped in an n-gram. The device may determine one or more skip n-grams using the skip value for the document. A skip n-gram, of the one or more skip n-grams, may include a sequence of one or more words or one or more characters with a set of occurrences in the document. The sequence of one or more words or one or more characters may include a skip value quantity of words or characters within the sequence. The device may extract one or more terms from the document based on the one or more skip n-grams. The device may provide information identifying the one or more terms.

Abstract: A device may receive, from a user device, a request for an automation service for a project. The device may analyze the request to identify one or more input keywords. The device may obtain, by using the one or more input keywords to search one or more data sources, project information for a set of projects and/or automation services information for a set of automation services. The device may determine one or more similar projects and/or one or more similar automation services by analyzing the project information and/or the automation services information. The device may provide, to the user device, a recommendation that identifies one or more automation services associated with the one or more similar projects and/or the one or more similar automation services.

Abstract: A device receives documents and previously answered questions associated with a restricted domain, and processes the documents and the previously answered questions to generate a corpus of searchable information. The device receives a question associated with the restricted domain, and processes the question, with a machine learning model or a rule-based classifier model, to determine a classification type for the question. The device manipulates the question to generate a query from the question, and processes the query, with an expansion technique, to generate an expanded query. The device utilizes the expanded query, with the corpus of searchable information, to identify candidate answers to the question, and processes the candidate answers and the classification type for the question, with a deep learning model, to generate scored and ranked candidate answers to the question. The device selects an answer from the scored and ranked candidate answers, and provides information indicating the answer.