Abstract: A device may identify standard parameters and real-time parameters associated with content of a content type, and may process the content type, the standard parameters, and the real-time parameters, with a parameter unification model, to generate derived parameters for the content. The device may process the derived parameters and the content type, with a multi-level linear regression machine learning model, to calculate a content score for the content, and may process the derived parameters and the content score, with a linear regression machine learning model, to calculate a quantity of f-NFTs to generate for the content and a divestment ratio. The device may create a unique reference to the content, and may create an NFT for the content based on the unique reference. The device may generate the quantity of f-NFTs for the content based on the NFT, and may provide the quantity of f-NFTs to a content exchange.

Abstract: A machine learning (ML) based code transformation system that transforms a source programming code developed using a source library for execution on a source platform into remediated code for execution on a target platform is disclosed. Metadata extracted from the source programming code is used to detect the source programming language, source libraries, and the source platform. The metadata also enables modularizing the source programming code based on the functionality and identifying a node from a plurality of nodes in a communication network to execute the various source code modules. A similarity map is generated mapping the source libraries to the target libraries and the source code modules that are incompatible with the target platform are identified and remediated with similar target code modules using the similarity map.

Abstract: A device may receive historical software data associated with a software development platform, and may train a neural network model, with the historical software data, to generate a trained neural network model. The device may receive, from the software development platform, historical test configuration data, and may train an execution model, with the historical test configuration data, to generate a trained execution model. The device may receive software data identifying software to be tested, and may process the software data, with the trained neural network model, to predict a set of test cases to execute for testing the software. The device may process the set of test cases, with the trained execution model, to identify configurations, scripts, and test targets for executing the set of test cases, and may perform one or more actions based on the set of test cases, the configurations, the scripts, and the test targets.

Abstract: A system may obtain a test utterance sample configured to test a virtual agent. The system may tokenize the test utterance sample into a sample tokens. The system may determine, based on a natural language processing framework, a synonymous set (synset) for at least one of the sample tokens. The synset may include a lemma tokens. The system may obtain a contextual corpus relevant to a natural language context of the virtual agent. The system may select a lemma token that is included in the corpus tokens. The system may generate a new test utterance comprising the selected lemma token. The system may insert the new test utterance in a test repository. The system may communicate the new test utterance to the virtual agent to test the virtual agent based on the new test utterance. If the virtual agent fails a test in respect to the new test utterance, the corpus of the virtual agent can be retrained.

Abstract: A system may obtain a test utterance sample configured to test a virtual agent. The system may tokenize the test utterance sample into a sample tokens. The system may determine, based on a natural language processing framework, a synonymous set (synset) for at least one of the sample tokens. The synset may include a lemma tokens. The system may obtain a contextual corpus relevant to a natural language context of the virtual agent. The system may select a lemma token that is included in the corpus tokens. The system may generate a new test utterance comprising the selected lemma token. The system may insert the new test utterance in a test repository. The system may communicate the new test utterance to the virtual agent to test the virtual agent based on the new test utterance. If the virtual agent fails a test in respect to the new test utterance, the corpus of the virtual agent can be retrained.