Bank of America Corporation

Vijay Kumar Battiprolu

Avinash Basavant Nigudkar

Lingaraj Gopalakrishnan

Anmol Puri

Rajesh Babu Vanamala

Aspects of the disclosure relate to s computing system that is configured to use heuristic and/or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and/or adaptable solution to detecting and preventing defects in software applications.

Intuitive defect prevention with swarm learning intelligence over blockchain network

A quantum computing system for determining diagnostic solutions for detected faults in computing devices using at least quantum annealing is described. The quantum computing system takes advantage of superposition and entanglement properties of qubits. A plurality of qubits is initialized into states representing historical data associated with historical faults and historical diagnostic solutions. Couplers entangle the plurality of qubits together based on the detected fault, generating a set of states in uniform superposition. Biases influence the energy levels of the set of states based on the detected fault. The quantum computing system measures a state with the lowest energy level within the set of states to determine diagnostic solutions to the detected fault. The system may further leverage a blockchain ledger for securely storing and accessing historical data associated with historical faults and historical diagnostic solutions.

TechWiki—computer diagnostics using quantum annealing and blockchain framework

A system includes an application server, a memory, and a processor. The application server runs one or more software threads. The memory stores snapshots of the system performance of the application server taken at periodic time intervals, a repository of previous stuck thread scenarios, and a plurality of thread healing scripts. The processor monitors server performance parameters, determines that a thread running on the application server is stuck, retrieves a set of snapshots of the system performance taken before the moment when it was determined that the thread is stuck, identifies from among the retrieved set of snapshots the oldest snapshot with a server performance parameter that exceeds a threshold, compares the server performance parameters in the identified oldest snapshot with the repository of previous stuck thread scenarios to identify a closest match, identifies a script for resolving the matched stuck thread scenario, and executes the identified script.

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Hyderabad Telangana, India

Vijay Kumar Battiprolu