Abstract: Arrangements for simulation generation and abnormality detection are provided. In some examples, a computing platform may identify a plurality of applications for analysis. The computing platform may execute a plurality of simulated scenarios for each application and, based on execution of the simulated scenarios, the computing platform may capture abnormality results for each application. The abnormality results, as well as application information and/or simulated scenarios may be stored. In some examples, the computing platform may compare deployed versions of each application to the captured abnormality data to identify an abnormality in a deployed version of an application. Based on the identified abnormality, the computing platform may evaluate the identified abnormality to determine whether it can be resolved automatically. If so, the computing platform may execute one or more commands modifying the deployed application to resolve the abnormality.

Abstract: Systems and methods for bias testing and remediation are disclosed. Decentralized Web Application Testing Systems use a Holochain framework to distribute testing workloads across Full Nodes and Lightning Nodes. A Holochain Node Management Application for configuring nodes, a UI Application creates test cases, and a Version Management System tracks changes. Test results are stored and analyzed in a Test Result Store, with a Bias Intelligence module detecting biases and generating additional test cases. A Consensus Algorithm validates test cases through decentralized nomination. An AI-Driven Defect Remediation System automates defect detection, root cause analysis, and remediation using AI modules. Machine learning algorithms identify patterns and anomalies, while NLP techniques generate code fixes. Predictive maintenance monitors application performance to preemptively address issues. A feedback loop mechanism continuously improves AI models through reinforcement learning.

Abstract: Systems and methods for bias testing and remediation are disclosed. Decentralized Web Application Testing Systems use a Holochain framework to distribute testing workloads across Full Nodes and Lightning Nodes. A Holochain Node Management Application for configuring nodes, a UI Application creates test cases, and a Version Management System tracks changes. Test results are stored and analyzed in a Test Result Store, with a Bias Intelligence module detecting biases and generating additional test cases. A Consensus Algorithm validates test cases through decentralized nomination. An AI-Driven Defect Remediation System automates defect detection, root cause analysis, and remediation using AI modules. Machine learning algorithms identify patterns and anomalies, while NLP techniques generate code fixes. Predictive maintenance monitors application performance to preemptively address issues. A feedback loop mechanism continuously improves AI models through reinforcement learning.

Abstract: A system for improving performance of a website is disclosed. The system detects web components associated with the website and determines conditional metrics. The conditional metrics indicate a range of conditions under which the performance of the website is evaluated. The system generates a set of test case scripts to emulate various user interactions with the website under various conditions according to one or more conditional metrics. The system executes a first test case script to emulate a first user interaction with a first web element under a first condition. The system determines that a result of the first test case script does not correspond to an expected output. In response, the system performs a corrective action, including updating a code portion associated with the first web element in the source code of the website to a code portion that is configured to provide the expected output.

Abstract: Arrangements for simulation generation and abnormality detection are provided. In some examples, a computing platform may identify a plurality of applications for analysis. The computing platform may execute a plurality of simulated scenarios for each application and, based on execution of the simulated scenarios, the computing platform may capture abnormality results for each application. The abnormality results, as well as application information and/or simulated scenarios may be stored. In some examples, the computing platform may compare deployed versions of each application to the captured abnormality data to identify an abnormality in a deployed version of an application. Based on the identified abnormality, the computing platform may evaluate the identified abnormality to determine whether it can be resolved automatically. If so, the computing platform may execute one or more commands modifying the deployed application to resolve the abnormality.