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: 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 device may obtain information identifying a base application. The device may extract a set of sub-application artifacts associated with the base application based on structural information associated with the base application. The device may define a set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a set of hash tuples for the set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a base composite identity of the base application based on the set of hash tuples. The device may store the base composite identity in a blockchain and in connection with storage of the base application in the blockchain to enable subsequent identification and verification of the base application.

Abstract: In some examples, Blockchain intelligent security implementation may include determining whether a Blockchain transaction has been initiated, generating, based on a determination that the Blockchain transaction has been initiated, a password, and storing the generated password. The stored password may be forwarded to a user associated with the Blockchain transaction. A further password may be received from the user associated with the Blockchain transaction, and validated, based on comparison of the stored password to the further password. Based on the validation of the further password, the Blockchain transaction may be processed.

Abstract: A device may receive information associated with first and second bug reports to be classified as duplicate or non-duplicate bug reports. The device may identify first and second descriptions associated with the first and second bug reports, respectively. The first and second descriptions may be different descriptions having a shared description type. The device may identify a neural network for encoding the first and second descriptions, based on the shared description type. The device may encode the first description into a first vector using the neural network, and may encode the second description into a second vector using the neural network. The device may classify the first and second bug reports as duplicate or non-duplicate bug reports based on the first vector and the second vector. The device may perform an action based on classifying the first and second bug reports as duplicate or non-duplicate bug reports.

Abstract: In some examples, temporal variation identification of regulatory compliance based robotic agent control may include ascertaining a temporal sequence of compliance specification text, where the temporal sequence may include time points and versions of the compliance specification text at the time points. For each time point of the temporal sequence of the compliance specification text, a compliance specification graph may be generated. Based on an analysis of each of the generated compliance specification graphs, changes in the temporal sequence of the compliance specification text may be determined. Further, an operation associated with a robotic agent may be controlled by the robotic agent and based on the determined changes in the temporal sequence of the compliance specification text.

Abstract: A device may determine probabilities for test scripts associated with a test to be executed on a software element, where a respective probability is associated with a respective test script, indicates a likelihood that the respective test script will be unsuccessful in a test cycle, and is determined based on historical test results, associated with the software element, for the respective test script. The device may generate, based on the probabilities, a test script execution order, of the test scripts, for the test cycle, and may execute, based on the test script execution order, the test on the software element in the test cycle. The device may dynamically generate, based on results for the test in the test cycle, an updated test script execution order, and may execute, based on the updated test script execution order, the test on the software element in the test cycle.

Abstract: A device may instrument a codebase associated with a software application. The device may execute a test on the instrumented codebase as the instrumented codebase executes, wherein the instrumented codebase generates test data based on the test being executed. The device may generate, based on the test data, a live graphical model of the codebase from a composite graphical model of the codebase, wherein the composite graphical model includes historical information, associated with the codebase, mapped to a graphical model of the codebase. The device may generate an extended reality rendered view of the live graphical model. The device may provide, to an extended reality device, the extended reality rendered view of the live graphical model for display by the extended reality device.

Abstract: A device may determine probabilities for test scripts associated with a test to be executed on a software element, where a respective probability is associated with a respective test script, indicates a likelihood that the respective test script will be unsuccessful in a test cycle, and is determined based on historical test results, associated with the software element, for the respective test script. The device may generate, based on the probabilities, a test script execution order, of the test scripts, for the test cycle, and may execute, based on the test script execution order, the test on the software element in the test cycle. The device may dynamically generate, based on results for the test in the test cycle, an updated test script execution order, and may execute, based on the updated test script execution order, the test on the software element in the test cycle.

Abstract: A device may receive a first command, included in a set of commands, to set a configuration parameter associated with performing feature extraction. The device may receive a second command, included in the set of commands, to set a corresponding value for the configuration parameter. The configuration parameter and the corresponding value may correspond to a particular feature metric that is to be extracted. The device may configure, based on the configuration parameter and the corresponding value, feature extraction for a corpus of documents. The device may perform, based on configuring feature extraction for the corpus, feature extraction on the corpus to determine the particular feature metric. The device may generate a feature vector based on performing the feature extraction. The feature vector may include the particular feature metric. The feature vector may include a feature identifier identifying the particular feature metric. The device may provide the feature vector.

Abstract: According to an example, customized visualization based intelligence augmentation may include accessing, based on a user request, a domain model, and mapping the user request to the domain model. Based on the mapping, a guided query that includes a relevant refinement question may be generated. A response may be received to the refinement question. Based on the received response, a refined user request may be generated, and classified into an intelligence augmentation category. Based on the classification, an intelligence augmentation analyzer may be accessed to analyze the refined user request to generate an insight output that is classified to a visualization. Based on the classification of the insight output to the visualization, responsive to the user request, a display of the visualization may be generated.

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: Systems, apparatuses, and methods directed to authentication. Authentication data may be determined by previous authentications of a plurality of products. Sensor data, related to a first product, may be received. A current authentication of the first product may be executed based on the sensor data and at an authentication device. The execution may include identifying the first product based on the sensor data. The execution may further include identifying whether the current authentication is substantially similar the previous authentications based on whether the first product is substantially similar to the plurality of products. The execution may further include that when the current authentication is identified as not being substantially similar to the one or more of the previous authentications, authenticating the first product according to remote data and the sensor data. Otherwise, the first product may be authenticated according to the authentication data.

Abstract: According to an example, with respect to knowledge graph based learning content generation, a plurality of concepts may be extracted from a plurality of documents. A word embedding similarity, and pointwise mutual information may be determined between each concept of the plurality of concepts. A concept similarity may be determined between each concept of the plurality of concepts, and a plurality of concept pairs that include similar concepts may be identified. A relationship may be determined between concepts for each concept pair of the plurality of concept pairs, and a determination may be made as to whether a concept of a concept pair is a pre-requisite of another concept of the concept pair to generate a knowledge graph. Based on the knowledge graph, a plurality of attributes and a learning goal for a learner, a concept of the plurality of concepts that matches the learning goal may be determined.

Abstract: According to an example, customized visualization based intelligence augmentation may include accessing, based on a user request, a domain model, and mapping the user request to the domain model. Based on the mapping, a guided query that includes a relevant refinement question may be generated. A response may be received to the refinement question. Based on the received response, a refined user request may be generated, and classified into an intelligence augmentation category. Based on the classification, an intelligence augmentation analyzer may be accessed to analyze the refined user request to generate an insight output that is classified to a visualization. Based on the classification of the insight output to the visualization, responsive to the user request, a display of the visualization may be generated.

Abstract: According to examples, development and production data based application evolution may include ascertaining a goal, development data, and production data for a specified release of an application. Development and production data based application evolution may further include determining, from the development data, a feature, a use-case associated with the feature, and a plurality of tasks associated with the use-case. Further, development and production data based application evolution may include determining, from the production data, production log data and user feedback, determining, from the production log data and the user feedback data, criteria, ascertaining, a weightage for each criterion of the criteria, and determining, based on the weightage, a task priority for each task of the plurality of tasks to be applied to a different release of the application.

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. The device may generate a report that indicates whether the one or more aspects of the ML application have been verified and may provide the report for display on an interface of the user device.

Abstract: A device may instrument a codebase associated with a software application. The device may execute a test on the instrumented codebase as the instrumented codebase executes, wherein the instrumented codebase generates test data based on the test being executed. The device may generate, based on the test data, a live graphical model of the codebase from a composite graphical model of the codebase, wherein the composite graphical model includes historical information, associated with the codebase, mapped to a graphical model of the codebase. The device may generate an extended reality rendered view of the live graphical model. The device may provide, to an extended reality device, the extended reality rendered view of the live graphical model for display by the extended reality device.

Abstract: A device may perform a static analysis of a codebase associated with a software application to determine a structure of the codebase and one or more metrics associated with the codebase. The device may generate a structured format of the codebase based on the structure of the codebase. The device may generate a domain specific language format of the codebase based on the structured format of the codebase and the one or more metrics associated with the codebase. The device may generate an extended reality rendered view of the domain specific language format of the codebase. The device may provide, to an extended reality device, the extended reality rendered view of the domain specific language format of the codebase.

Abstract: A method may include receiving, by a device, an input sample of textual content. The method may include identifying, by the device, a comparison sample that is semantically similar to the input sample. The comparison sample may be identified based on a similarity score, of the comparison sample and the input sample, satisfying a semantic similarity threshold. The method may include identifying, by the device, a plurality of output samples of textual content based on acceptance information corresponding to the plurality of output samples and the comparison sample. The acceptance information may be determined based on a user input regarding similarity or relevance of the plurality of output samples and the comparison sample, and the user input may be received before the input sample is received.