Abstract: A device may obtain text to be processed to extract constraints corresponding to an object in the text. The constraints may define values permitted to be associated with the object. The device may extract the constraints based on identifying patterns in the text. The device may generate, based on the constraints, positive test data and negative test data for testing values for the object. The positive test data may include a first value that satisfies each of the constraints, and the negative test data may include a second value that violates at least one of the constraints. The device may provide information that identifies the positive test data and the negative test data.

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: 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 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: A device may receive first information that identifies an input associated with a virtual agent application executing on a user device. The virtual agent application may provide an interface for a project involving a plurality of user devices. The device may determine, based on the first information that identifies the input, a first response based on second information. The device may determine, based on at least one of the first information that identifies the input or the first response and without user input, a second response. The device may provide, to the virtual agent application of the user device, fourth information that identifies at least one of the first response or the second response.

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 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 receive a first sample. The device may identify a plurality of second samples that are similar to the first sample. The plurality of second samples may correspond to service tickets that have previously been processed. The device may obtain or determine log information identifying a plurality of events associated with the first sample. The device may determine respective probabilities of occurrence corresponding to the plurality of second samples. A probability of occurrence, for a particular second sample, may identify a likelihood of receiving a corresponding service ticket within a particular time period of occurrence of the plurality of events. The device may assign ranks to the plurality of second samples based on the respective probabilities of occurrence. The device may provide information identifying the ranks and the plurality of second samples.

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.

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 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 include one or more processors. The device may receive text to be processed to identify input entities included in the text. The device may identify text sections of the text. The device may generate a list of terms included in the text sections of the text. The device may perform one or more feature extraction techniques, on the terms included in the text sections, to identify the input entities included in the text. The device may generate information that identifies the input entities included in the text, based on performing the one or more feature extraction techniques. The device may provide the information that identifies the input entities included in the text.

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: According to examples, intelligent scheduling and work item allocation may include ascertaining work items, and classifying the work items by using classification rules to map each of the work items to a corresponding type of work item based on attributes associated with the work items to generate classified work items. Intelligent scheduling and work item allocation may include prioritizing the classified work items by using prioritization rules to determine a sequence of the classified work items based on the attributes and classification of the work items to generate prioritized work items. Intelligent scheduling and work item allocation may include scheduling the classified and prioritized work items by using scheduling rules to determine times of processing of the classified and prioritized work items, and allocating the classified and prioritized work items by using allocation rules to determine resources that are to process the classified and prioritized work items.

Abstract: A device may include one or more processors. The device may receive text to be processed to identify input entities included in the text. The device may identify text sections of the text. The device may generate a list of terms included in the text sections of the text. The device may perform one or more feature extraction techniques, on the terms included in the text sections, to identify the input entities included in the text. The device may generate information that identifies the input entities included in the text, based on performing the one or more feature extraction techniques. The device may provide the information that identifies the input entities included in the text.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for natural language processing of unstructured text are disclosed. In one aspect, a method includes the actions of receiving one or more unstructured data entries that each include one or more sentences, are each associated with an entity, and are each from a user. The actions further include parsing the one or more sentences. The actions further include determining one or more classifications of each unstructured data entry. The actions further include determining a sentiment. The actions further include accessing structured data. The actions further include defining one or more groups of users based on the structured data, wherein each of the one or more groups shares a common characteristic in the structured data. The actions further include determining sentiments to associate with the group.

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: A device may obtain text to be processed to extract constraints corresponding to an object in the text. The constraints may define values permitted to be associated with the object. The device may extract the constraints based on identifying patterns in the text. The device may generate, based on the constraints, positive test data and negative test data for testing values for the object. The positive test data may include a first value that satisfies each of the constraints, and the negative test data may include a second value that violates at least one of the constraints. The device may provide information that identifies the positive test data and the negative test data.

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 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.