Intuitive defect prevention with swarm learning intelligence over blockchain network
AssigneeBank of America Corporation
InventorsAvinash Nigudkar, Lingaraj Gopalakrishnan, Anmol Puri, Vijay Kumar Battiprolu
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Abstract
Aspects of the disclosure relate to a 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.
Claims20
A swarm learning (SL) computing system that analyzes cognitive response to application defects, the computing system comprising:
- a swarm of boids comprising agents communicatively coupled on a communications network for peer-to-peer communication without requiring a centralized server;
- a virtual reality (VR) simulation headset that collects cognitive responses of a user;
- a blockchain comprising a plurality of blocks corresponding to a plurality of software application failure logs and defects data, wherein the plurality of software application failure logs indicate events associated with a plurality of software applications that failed, and wherein the defects data corresponds to what caused the plurality of software applications to fail;
- and
- a particle swarm optimization (PSO) model that categorizes the defects as either a false positive defect or a true positive defect and to generate a defect prevention learning dataset to test one or more software applications,
- wherein the VR simulation headset immersively displays step-by-step guidance for fixing the true positive defect, and
- wherein the defect prevention learning dataset provides a feedback loop to an input of a machine learning model to iteratively generate output used in an evolving VR simulation via the VR simulation headset to prevent a future defect, and wherein the peer-to-peer communication provides interoperability between the defect prevention learning dataset and the blockchain.
A method for analyzing cognitive response to application defects using a plurality of nodes communicatively coupled to a peer-to-peer network, wherein the plurality of nodes correspond to a swarm of boids comprising agents, the method comprising:
- (a) collecting, by a virtual reality (VR) simulation device, cognitive responses of a user;
- (b) accessing a blockchain, which comprises a plurality of blocks corresponding to a plurality of software application failure logs indicating events associated with software application failures and defects data that caused the software application failures;
- (c) categorizing, by a particle swarm optimization (PSO) model, the defects data as either a false positive defect or a true positive defect;
- (d) generating, by the PSO model, a defect prevention learning dataset to test one or more software applications;
- (e) displaying, by the VR simulation device, step-by-step guidance for fixing the true positive defect;
- and
- (f) providing a feedback loop, using the defect prevention learning dataset, to an input of a machine learning model to iteratively generate output used in an evolving VR simulation via the VR simulation device to prevent a future defect,
- wherein peer-to-peer communication provides interoperability between the defect prevention learning dataset and the blockchain.
A non-transitory computer-readable medium storing instruction that, when executed by one or more computer processors, cause the one or more computer processors to perform a method comprising steps of:
- (a) collecting, by a virtual reality (VR) simulation device, cognitive responses of a user;
- (b) accessing a blockchain, which comprises a plurality of blocks corresponding to a plurality of software application failure logs indicating events associated with software application failures and defects data that caused the software application failures;
- (c) categorizing, by a particle swarm optimization (PSO) model, the defects data as either a false positive defect or a true positive defect;
- (d) generating, by the PSO model, a defect prevention learning dataset to test one or more software applications;
- (e) displaying, by the VR simulation device, step-by-step guidance for fixing the true positive defect;
- and
- (f) providing a feedback loop, using the defect prevention learning dataset, to an input of a machine learning model to iteratively generate output used in an evolving VR simulation via the VR simulation device to prevent a future defect,
- wherein peer-to-peer communication provides interoperability between the defect prevention learning dataset and the blockchain.
Dependencies are inferred from the claim text.