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Onclick predictability by leveraging Connected Clients Disclosure Number: IPCOM000247792D
Publication Date: 2016-Oct-06
Document File: 2 page(s) / 29K

Publishing Venue

The Prior Art Database


Reuse learning from success/failure of a user operation working on the client side of a client-server application running under a specific configuration and environment to predict if the same operation initiated by a different user would succeed or not.

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Onclick predictability by leveraging Connected Clients

In a Client Server based software deployment environment, currently a failure at one client is known only to that Client and its corresponding Server.

Example, in Collaborative Software Lifecycle Management environment, the failure at any of the Source Code Management (SCM) Clients, for example Eclipse is recorded in the specific user / client logs or the server logs. The other clients in the same SCM environment do not know of that failure and may attempt the same operation and hit the same failure.

This failure at the second client is additional load or noise to the Server and also could consume time and resources on the client machine before it ends in the inevitable failure that happened at the other client.

In such cases, cumulative learning from different clients in the network, could be assessed and provide on-click predictability, user experience of other clients. Our intention is to make this possible by establishing a learning module overlooking the connected clients - something we would like to call as "Internet of Clients".

Our feature is to reuse learning from connected clients to avoid conducive failure noise, unnecessary resource usage on the server and other clients in the network.

If a user operation could be validated against information from the connected clients before the request is sent to the server, it would pro-actively avoid the error and reduce requests to the server.

Below are examples of some operations that could be initiated from a Client and on a user machine that could fail due to say lack of sufficient resources to complete it:
a. Loading a specific chunk of source code data.

b. Import of large shared data

When a similar operation is performed by a different user on that SCM User Network, the learning is used to verify the user environment, preempt the operation if requisites are not met and suggest the user to take corrective action...