NATURAL LANGUAGE UNIFICATION BASED ROBOTIC AGENT CONTROL

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. 1. A natural language unification based robotic agent control apparatus comprising: ascertain, by a robotic agent, an image of an object or an environment, and', 'ascertain a plurality of natural language insights for the image;, 'an insight analyzer, executed by at least one hardware processor, to'} 'determine semantic relatedness between each insight of the plurality of insights;', 'a semantic relatedness analyzer, executed by the at least one hardware processor, to'} 'generate, based on the determined semantic relatedness, a semantic relatedness graph for the plurality of insights;', 'a semantic relatedness graph generator, executed by the at least one hardware processor, to'} 'identify, for each insight of the plurality of insights, at least one central concept;', 'a central concepts identifier, executed by the at least one hardware processor, to'} 'cluster, based on the semantic relatedness graph and the identified at least one central concept, the plurality of insights to generate at least one insights cluster;', 'an insights cluster generator, ...

Natural language eminence based robotic agent control

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.

AUTOMATED FUNCTIONAL DIAGRAM GENERATION

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. 1. A device , comprising: [ 'the test script document including a set of test scripts;', 'obtain a test script document,'}, one or more terms, of the set of terms, being located within an n-gram of the test script document,', 'the n-gram being identified using the skip n-gram term extraction technique;, 'the one or more term extraction techniques include a skip n-gram term extraction technique,'}, '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,'}, 'a relationship between a plurality of terms, of the set of terms, being identified using hierarchy formation;', 'process the test script document to perform hierarchy formation for results of performing term extraction,'}, 'generate a functional diagram of the test script document based on the results of performing term extraction and results of performing hierarchy formation; and', 'provide information identifying the functional diagram., 'one or more processors to2. The device of claim 1 , where the one or more processors claim 1 , when ...

Automated term extraction

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.

Generating a test script execution order

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.

AUTOMATED TERM EXTRACTION

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. 120-. (canceled)21. A method , comprising: 'the term extraction for extracting one or more terms;', 'performing, by a device, term extraction for a test script document using one or more term extraction techniques,'}performing, by the device, hierarchy formation for the test script document based on results of performing the term extraction,generating, by the device, a functional diagram of the test script document based on results of performing the term extraction and performing the hierarchy formation; andproviding, by the device and to via a user interface, information identifying the functional diagram of the test script document.22. The method of claim 21 , wherein the one or more term extraction techniques include at least one of:a skip n-gram based term extraction technique,a regular expression pattern based term extraction technique,a technical terminology identification based term extraction technique,a glossary based term extraction technique,a collocation of words based term extraction technique,a multi-word expressions based term extraction technique,a keywords based term extraction technique,a key-phrases based term extraction technique, ora topics based term extraction technique.23. The method of claim 21 , ...

Verification of applications that utilize artificial intelligence

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

CONSTRAINT EXTRACTION FROM NATURAL LANGUAGE TEXT FOR TEST DATA GENERATION

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. 1. A device , comprising: [ 'the one or more constraints defining values permitted to be associated with the object;', 'obtain text to be processed to extract one or more constraints corresponding to an object in the text,'}, 'the one or more patterns including a relational operator and a numeric value;', 'extract the one or more constraints based on identifying one or more patterns in the text,'}, the positive test data including a first value that satisfies each of the one or more constraints,', 'the negative test data including a second value that violates at least one of the one or more constraints; and, 'generate, based on the one or more constraints, positive test data and negative test data for testing values for the object,'}, 'provide information that identifies the positive test data and the negative test data., 'one or more processors to2. The device of claim 1 , where the one or more processors are further to:obtain existing test data for testing values associated with the object;determine whether the existing test data satisfies the one or more constraints; and 'provide information that indicates whether the existing test data satisfies the one or more constraints.', 'where the one or more processors, when providing the information that identifies the positive test data and the ...

Utilizing object oriented programming to validate machine learning classifiers and word embeddings

In some implementations, a device may receive a machine learning model to be tested. The device may process the machine learning model, with generalization testing methods, to determine generalization which identifies responsiveness of the machine learning model to varying inputs. The device may process the machine learning model, with robustness testing methods, to determine robustness which identifies responsiveness of the machine learning model to improper inputs. The device may process the machine learning model, with an interpretability testing method, to determine decisions of the machine learning model. The device may calculate a score for the machine learning model based on the generalization data, the robustness data, and the interpretability data. The device may perform one or more actions based on the score for the machine learning model.

USING SIMILARITY ANALYSIS AND MACHINE LEARNING TECHNIQUES TO MANAGE TEST CASE INFORMATION

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. 1. A device , comprising: [ 'the test case information including at least one of test case description information or test case environment information;', 'obtain test case information for a set of test cases,'}, '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;', '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;', 'receive feedback information associated with at least a portion of the set of overall similarity scores;', 'modify one or more values associated with the machine learning technique based on the feedback information;', 'determine a new set of overall similarity scores for the set of test case groups by using the machine learning technique with the one or more modified values to analyze the set of test case field groups;', 'update a data structure that stores the test case information to establish one or more associations between the ...

PLATFORM FOR SUPPORTING MULTIPLE VIRTUAL AGENT APPLICATIONS

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. 1. A device , comprising: [ 'the virtual agent application providing an interface for a project involving a plurality of user devices;', 'receive first information that identifies an input associated with a virtual agent application executing on a user device,'}, 'determine, based on the first information that identifies the input, a first response based on second information;', the second response being associated with third information,', 'the second information being associated with a first resource, and', 'the third information being associated with a second resource that is different than the first resource; and, '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,'}, '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., 'one or more processors to2. The device of claim 1 , where the one or more processors are further to:identify a set of results based on the first information that identifies the input;determine a first ranking of the set of results;receive, from the user device, fifth information that identifies a user-defined ranking of the set of results; 'the one or ...

DUPLICATE AND SIMILAR BUG REPORT DETECTION AND RETRIEVAL USING NEURAL NETWORKS

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. 1. A device , comprising: receive information associated with a first bug report and a second bug report to be classified as duplicate bug reports or non-duplicate bug reports;', 'the first description and the second description being different descriptions having a shared description type;', 'identify a first description, associated with the first bug report, and a second description associated with the second bug report,'}, 'identify a neural network, of a plurality of different types of neural networks, for encoding the first description and the second description, based on the shared description type;', 'encode the first description into a first vector using the neural network;', 'encode the second description into a second vector using the neural network;', 'classify the first bug report and the second bug report as duplicate bug reports or non-duplicate bug reports based on the first vector and the second vector; and', 'perform an action based on classifying the first bug report and the second bug report as duplicate bug reports or non- ...

Test automation using multiple programming languages

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.

DEVICE-BASED VISUAL TEST AUTOMATION

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. 1. A device , comprising: [ 'the test script being written using first text or first program code;', 'receive a test script that includes first information identifying a first element to be displayed on a user interface and second information identifying a set of test steps to test the user interface,'}, 'process the first text or the first program code of the test script using a processing technique to identify the first information and the second information;', the first element being identified without using second program code underlying the user interface,', 'the first element being identified based on a type of the first element, second text associated with the first element, or a visual relationship between the first element and a second element;, 'identify the first element on the user interface based on identifying the first information and the second information included in the test script,'}, 'the set of coordinates identifying a position of the first element on the user interface;', 'determine a set of coordinates for the first element on the user interface based on ...

Input entity identification from natural language text information

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.

Recruitment process graph based unsupervised anomaly detection

In some examples, recruitment process graph based unsupervised anomaly detection may include obtaining log data associated with a recruitment process for a plurality of candidates, and generating knowledge graphs and graph embeddings. The graph embeddings may be trained to include a plurality of properties such that graph embeddings of genuine candidate hires and fraudulent candidate hires are appropriately spaced in a vector space. The trained graph embeddings may be clustered to generate a plurality of embedding clusters that include a genuine candidate cluster, and a fraudulent candidate cluster. For a new candidate graph embedding for a new candidate, a determination may be made as to whether the new candidate graph embedding belongs to the genuine candidate cluster, to the fraudulent candidate cluster, or to an anomalous cluster, and instructions may be generated to respectively retain or suspend the new candidate.

Intelligent scheduling and work item allocation

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.

BLOCKCHAIN INTELLIGENT SECURITY IMPLEMENTATION

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. 1. A Blockchain intelligent security implementation apparatus comprising: determine whether a Blockchain transaction has been initiated; a password generator, executed by the at least one hardware processor, to', 'generate, based on a determination that the Blockchain transaction has been initiated, a password;, 'a Blockchain transaction analyzer, executed by at least one hardware processor, to'}a password recorder, executed by the at least one hardware processor, to store the generated password; 'forward the stored password to a user associated with the Blockchain transaction;', 'a password communicator, executed by the at least one hardware processor, to'} receive a further password from the user associated with the Blockchain transaction, and', 'validate, based on comparison of the stored password to the further password received from the user associated with the Blockchain transaction, the further password; and, 'a password validator, executed by the at least one hardware processor, to'} 'process, based on the validation of the further password, the Blockchain transaction.', 'a Blockchain transaction processor, executed by the at least one hardware processor, to'}2. The apparatus according to claim 1 , wherein the password generator is executed by the at least one hardware processor to generate claim 1 , based on the determination ...

Human centered computing based digital persona generation

In some examples, human centered computing based digital persona generation may include generating, for a digital persona that is to be generated for a target person, synthetic video files and synthetic audio files that are combined to generate synthetic media files. The digital persona may be generated based on a synthetic media file. An inquiry may be received from a user of the generated digital persona. Another synthetic media file may be used by the digital persona to respond to the inquiry. A real-time emotion of the user may be analyzed based on a text sentiment associated with the inquiry, and a voice sentiment and a facial expression associated with the user. Based on the real-time emotion of the user, a further synthetic media file may be utilized by the digital persona to continue or modify a conversation between the generated digital persona and the user.

GENERATING TEST DATA FROM SAMPLES USING NATURAL LANGUAGE PROCESSING AND STRUCTURE-BASED PATTERN DETERMINATION

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. 1. A device , comprising: receive a plurality of samples that include textual content;', 'the pattern information identifying shared unit values, of the unit values, that are included in at least two samples of the plurality of samples;', 'the structural units identifying characteristics of the plurality of samples to be used to identify pattern information relating to the plurality of samples,'}, 'extract unit values, corresponding to structural units, from the plurality of samples,'}, 'the one or more structural representations identifying the pattern information;', 'generate one or more structural representations based on the unit values,'}, the one or more additional samples including at least one of the unit values, and', 'the one or more additional samples being generated based on the pattern information; and, 'generate one or more additional samples based on the one or more structural representations,'}, 'output the one or more additional samples., 'one or more processors to2. The device of claim 1 , where the plurality of samples ...

Facilitating merging of concept hierarchies

A device may identify a first concept hierarchy of a first multimedia presentation and a second concept hierarchy of a second multimedia presentation. The device may determine a set of concepts associated with the first concept hierarchy and the second concept hierarchy and may determine a plurality of similarity scores associated with the set of concepts. The device may generate a new concept hierarchy based on the plurality of similarity scores, the first concept hierarchy, and the second concept hierarchy by: performing a first process to merge a concept with an additional concept; performing a second process to position a concept sequentially before or after an additional concept; performing a third process to position a concept and a different concept to sequentially follow an additional concept; and performing a fourth process to position a concept to sequentially follow an additional concept and a different concept.

Verification of applications that utilize artificial intelligence

Summarizing, the application relates to a method that may comprise: receiving, by a device and from a user device, a request to verify a machine learning (ML) application, wherein verification of the ML application is to be performed using a metamorphic testing procedure; determining, by the device, a type of ML process used by the ML application after receiving the request; selecting, by the device, one or more metamorphic relations (MRs), that are to be used for performing the metamorphic testing procedure, based on the type of ML process used by the ML application; receiving, by the device, test data that is to be used to test the ML application, wherein the test data is based on the one or more MRs; performing, by the device and 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; generating, by the device, a report that includes verification results data indicating whether the one or more aspects of the ML application have been verified; and providing, by the device, the report for display on an interface of the user device; as well as a a computer program product and a device.

Platform for supporting multiple virtual agent applications

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. 0~ (I0 0 !i) m :0 00 814-4- 4 4 Mz zz r-I (2D L 0 l- (D -0 (D) 00D LO ' c O I- co 0) ci) cn rI) Cl) Cl 0 0 - L -a-> L) -)4 (D- -n a- a( ) CI)) 'n > C) U,, 'a) C)C C))0 V--

Automated term extraction

A device, including one or more processors that obtain a test script document, the test script document including a set of words or a set of characters, determine a skip value for the document, the skip value being a quantity of arbitrary words or a quantity of arbitrary characters that are to be skipped in an n-gram, determine one or more skip n grams using the determined skip value for the test script document, a skip n-gram, of the one or more skip n-grams, including a sequence of one or more words or one or more characters with a plurality of occurrences in the test script document, the sequence of one or more words or one or more characters including the determined skip value quantity of words or characters within the sequence, extract one or more terms from the test script document based on the one or more skip n-grams, a term associated with the skip n gram corresponding to the determined skip value quantity of words or characters within the sequence, perform, based on extracting the one or more terms, hierarchy information on the test script document to identify one or more relationships for the one or more terms, provide information identifying the one or more terms, generate a functional diagram representing the test script document using the one or more terms based on extracting the one or more terms and based on the hierarchy formation, and where the one or more processors, when providing information identifying the one or more terms provide information identifying the functional diagram. 0 cu U- U0 cu cu -i2i0 , (oun -~ (9 C: Cu CL -C 0' LU cu) cn-

Device based visual test automation

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.

Generating test data from samples using natural language processing and structure-based pattern determination

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.

Constraint extraction from natural language text for test data generation

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.

Platform for supporting multiple virtual agent applications

A device, including one or more processors that receive first information that identifies an input associated with a virtual agent application executing on a user device, the virtual agent application providing an interface for a project involving one or more project steps and a plurality of user devices, the input including a query relating to the project, identify, based on the query, second information associated with the project, determine, based on the second information, a first response, the first response including an answer to the query, identify, based on the query and the answer to the query, and without user input, third information that identifies another project involving one or more project steps determined as having not been performed in the project, determine, based on the third information, a second response, the second response including a recommendation to perform the one or more project steps determined as having not been performed, the second information associated with a first resource, and the third information associated with a second resource that is different to the first resource, provide, to the virtual agent application of the user device, fourth information that identifies the first response and the second response, receive input identifying that the steps determined as not performed in the project are to be performed, and automatically perform the one or more project steps determined as having not been performed. c (1) 0 CU) c -7 -C 0 C (1) c4- CL 0 c U)cC)) 0 o-n o E U)m .I 0 0 0 IE-C c C/) (_ 40 -- cu a) c LO cooo U/) U)7U) U) /) CU U) C: U) c I- C -0 C ?1U = ') a) - C '-*3 u 2 U) CLa)) *n > a)0 > a) E=0

Duplicate and similar bug report detection and retrieval using neural networks

A device, including one or more processors that receive information associated with a first bug report and a second bug report for classification as duplicate bug reports or non duplicate bug reports, identify a first set of descriptions, associated with the first bug report, and a second set of descriptions associated with the second bug report, each description, included in the first set of descriptions, and each corresponding description, included in the second set of descriptions, sharing a description type, identify a neural network, of a plurality of different types of neural networks, for encoding the set of descriptions and the second set of descriptions, based on whether the shared description type is an unstructured data type, a short description type, a long description type, or a structured description type, encode the first set of descriptions into a first set of vectors using the neural network, encode the second set of descriptions into a second set of vectors using the neural network, generate, by combining the first set of vectors, a first overall vector for the first bug report, generate, by combining the second set of vectors, a second overall vector for the second bug report, classify, by comparing the first overall vector and the second overall vector, the first bug report and the second bug report as duplicate bug reports or non-duplicate bug reports, and perform an action based on classifying the first bug report and the second bug report as duplicate bug reports or non-duplicate bug reports. cu C)C 00 00L

System behavior profiling-based dynamic competency analysis

In some examples, system behavior profiling-based dynamic competency analysis may include identifying a plurality of software generation entities that have contributed to a module of a system, and generating an index to associate each software generation entity of the plurality of software generation entities. Execution links may be extracted from execution traces of the system, and an execution competency list may be generated. A dynamic competency score may be generated for each software generation entity for the system, and an overall dynamic competency score and a combined competency score may be determined. A software generation entity role may be obtained for a new application, and a software generation entity of the plurality of software generation entities may be identified to perform the software generation entity role. Development of the new application may be implemented using the identified software generation entity.

Human centered computing based digital persona generation

In some examples, human centered computing based digital persona generation may include generating, for a digital persona that is to be generated for a target person, synthetic video files and synthetic audio files that are combined to generate synthetic media files. The digital persona may be generated based on a synthetic media file. An inquiry may be received from a user of the generated digital persona. Another synthetic media file may be used by the digital persona to respond to the inquiry. A real-time emotion of the user may be analyzed based on a text sentiment associated with the inquiry, and a voice sentiment and a facial expression associated with the user. Based on the real-time emotion of the user, a further synthetic media file may be utilized by the digital persona to continue or modify a conversation between the generated digital persona and the user.

Generating a test script execution order

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.

Generating a test script execution order

Using similarity analysis and machine learning techniques to manage test case information

A device, including one or more processors that obtain test case information for a set of test cases, the test case information including at least one of test case description information or test case environment information, sort the test case information for the set of test cases into a set of test case groups, where each test case group includes test case information for two or more test cases, 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, where the set of similarity analysis techniques includes at least one of a tuple-based similarity analysis technique, a data structure-driver similarity analysis technique, an approximation-based similarity analysis technique, or a reduction based similarity analysis technique, 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, receive feedback information associated with at least a portion of the set of overall similarity scores, modify one or more values associated with the machine learning technique based on the feedback information, determine a new set of overall similarity scores for the set of test case groups by using the machine learning technique with the one or more modified values to analyze the set of test case field groups, update a data structure that stores the test case information to establish one or more associations between the test case information and the new set of overall similarity scores, and process a request from a user device using the one or more associations between the set of test cases and the new set of overall similarity scores, the request including at least one of a request to consolidate the test case information, or a request for test cases including parameters identifying characteristics of test cases. co co co CN cn 0 5 c N 0 0 U) (N 0 2 t ...

Tailings centrifugation for accelerated assessment of tailings treatments and dewatering

The present disclosure provides processes, systems, devices and techniques for assessing and predicting settling behavior of oil sands tailings. Various systems, processes, devices and techniques are described that allow the assessment or prediction of the settling behavior of oil sands tailings. A centrifugal test system can be used to provide conditions for accelerated separation of solids from water of an oil sands tailings sample, and the separation can be monitored, for example by acquiring images at certain points in time during the test.

Tailings centrifugation for accelerated assessment of tailings treatments and dewatering

The present disclosure provides processes, systems, devices and techniques for assessing and predicting settling behavior of oil sands tailings. Various systems, processes, devices and techniques are described that allow the assessment or prediction of the settling behavior of oil sands tailings. A centrifugal test system can be used to provide conditions for accelerated separation of solids from water of an oil sands tailings sample, and the separation can be monitored, for example by acquiring images at certain points in time during the test.

Input entity identification from natural language text information

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.