Abstract: A system for generating non-fungible token-based test suites from design diagrams comprises a processor associated with a server. The processor is configured to obtain image objects representing a workflow associated with a design diagram and a user profile of a user. The processor is configured to generate a design non-fungible token (NFT) associated with the design diagram to verify a design diagram ownership. The processor is configured to convert the image objects of the design diagram into corresponding text objects and to process the corresponding text objects of the design diagram to generate a plurality of text datasets corresponding to the workflow of the design diagram, analyze the plurality of the text datasets and generate a test suite which comprises a set of test scripts corresponding to the design diagram, and generate a test case NFT associated with the test suite to verify a test suite ownership.

Abstract: Aspects of the disclosure relate to conducting automated web service testing in a continuous integration and delivery test deployment environment using artificial intelligence (AI) generated test data. In some embodiments, a computing platform may receive, from a developer computing platform, a test code request, receive, from a web service computing platform, a training data set, configure a test data set based on the training data set and the test code request, use AI engine to apply one or more corrections to the test data set based on the test code request and to produce a corrected test data set, execute the test code using the corrected test data set to produce test code output results, and send, to the developer computing platform, the test code output results.

Abstract: Aspects of the disclosure relate to conducting automated web service testing in a continuous integration and delivery test deployment environment using artificial intelligence (AI) generated test data. In some embodiments, a computing platform may receive, from a developer computing platform, a test code request, receive, from a web service computing platform, a training data set, configure a test data set based on the training data set and the test code request, use AI engine to apply one or more corrections to the test data set based on the test code request and to produce a corrected test data set, execute the test code using the corrected test data set to produce test code output results, and send, to the developer computing platform, the test code output results.

Abstract: A system for generating non-fungible token-based test suites from design diagrams comprises a processor associated with a server. The processor is configured to obtain image objects representing a workflow associated with a design diagram and a user profile of a user. The processor is configured to generate a design non-fungible token (NFT) associated with the design diagram to verify a design diagram ownership. The processor is configured to convert the image objects of the design diagram into corresponding text objects and to process the corresponding text objects of the design diagram to generate a plurality of text datasets corresponding to the workflow of the design diagram, analyze the plurality of the text datasets and generate a test suite which comprises a set of test scripts corresponding to the design diagram, and generate a test case NFT associated with the test suite to verify a test suite ownership.

Abstract: Aspects of the disclosure relate to conducting automated web service testing in a continuous integration and delivery test deployment environment using artificial intelligence (AI) generated test data. In some embodiments, a computing platform may receive, from a developer computing platform, a test code request, receive, from a web service computing platform, a training data set, configure a test data set based on the training data set and the test code request, use AI engine to apply one or more corrections to the test data set based on the test code request and to produce a corrected test data set, execute the test code using the corrected test data set to produce test code output results, and send, to the developer computing platform, the test code output results.

Abstract: Aspects of the disclosure relate to conducting automated web service testing in a continuous integration and delivery test deployment environment using artificial intelligence (AI) generated test data. In some embodiments, a computing platform may receive, from a developer computing platform, a test code request, receive, from a web service computing platform, a training data set, configure a test data set based on the training data set and the test code request, use AI engine to apply one or more corrections to the test data set based on the test code request and to produce a corrected test data set, execute the test code using the corrected test data set to produce test code output results, and send, to the developer computing platform, the test code output results.

Abstract: An apparatus includes a memory and a hardware processor. The memory stores a plurality of logging rules. Each logging rule assigned to a tier of a multi-tier platform. The processor receives source code for an application configured to execute on a plurality of tiers of the multi-tier platform and detects, within the source code, an entry point and an exit point for a tier of the plurality of tiers. The processor determines, based on the plurality of logging rules, a first attribute that is to be logged during execution in the tier and a second attribute that is not to be logged during execution in the tier and inserts, between the entry point and the exit point in the source code, logging code that, when executed, logs the first attribute and hides the second attribute.

Abstract: Aspects of the disclosure relate to conducting automated web service testing in a continuous integration and delivery test deployment environment using artificial intelligence (AI) generated test data. In some embodiments, a computing platform may receive, from a developer computing platform, a test code request, receive, from a web service computing platform, a training data set, configure a test data set based on the training data set and the test code request, use AI engine to apply one or more corrections to the test data set based on the test code request and to produce a corrected test data set, execute the test code using the corrected test data set to produce test code output results, and send, to the developer computing platform, the test code output results.

Abstract: Aspects of the disclosure relate to conducting automated web service testing in a continuous integration and delivery test deployment environment using artificial intelligence (AI) generated test data. In some embodiments, a computing platform may receive, from a developer computing platform, a test code request, receive, from a web service computing platform, a training data set, configure a test data set based on the training data set and the test code request, use AI engine to apply one or more corrections to the test data set based on the test code request and to produce a corrected test data set, execute the test code using the corrected test data set to produce test code output results, and send, to the developer computing platform, the test code output results.

Abstract: An apparatus includes a memory and a hardware processor. The memory stores a plurality of logging rules. Each logging rule assigned to a tier of a multi-tier platform. The processor receives source code for an application configured to execute on a plurality of tiers of the multi-tier platform and detects, within the source code, an entry point and an exit point for a tier of the plurality of tiers. The processor determines, based on the plurality of logging rules, a first attribute that is to be logged during execution in the tier and a second attribute that is not to be logged during execution in the tier and inserts, between the entry point and the exit point in the source code, logging code that, when executed, logs the first attribute and hides the second attribute.

Abstract: An apparatus includes a memory and a hardware processor. The memory stores a plurality of reprocessing rules. The processor receives a request message from a user device. The processor communicates a second request to a first resource and a third request to a second resource. The processor determines that a response to the second request was not received. The processor increases the first timeout. The processor communicates the second request to the first resource after increasing the first timeout, receives a response to the second request, and determines that a response to the third request was not received. The processor increases the reconnect parameter. The processor communicates the third request to the second resource after increasing the reconnect parameter, receives a response to the third request, generates a response message to the request message, and communicates the response message.

Abstract: An apparatus includes a memory and a hardware processor. The memory stores a plurality of reprocessing rules. The processor receives a request message from a user device. The processor communicates a second request to a first resource and a third request to a second resource. The processor determines that a response to the second request was not received. The processor increases the first timeout. The processor communicates the second request to the first resource after increasing the first timeout, receives a response to the second request, and determines that a response to the third request was not received. The processor increases the reconnect parameter. The processor communicates the third request to the second resource after increasing the reconnect parameter, receives a response to the third request, generates a response message to the request message, and communicates the response message.