Software Patch Matching via Task Operation Encoding
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Solution Overview
Problem
Existing software update methods often result in unnecessary patch installations, leading to increased computing costs, downtime, and installation failures due to the lack of matching software patch recommendations based on user device behavior.
Innovation Solution
A computer-implemented method that abstracts and encodes software patch test cases into task operations and parameters, uses machine learning to generate lists of frequent operation items, and clusters parameters, enabling user devices to determine matching patches and automate installations based on actual software behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software patches are pushed to all customers for direct download and installation, then software defects can be addressed, but unnecessary patch installations increase computing costs and downtime
Solution Approach 1:
The system performs preliminary analysis of user device behavior patterns and software patch requirements before pushing patches. By pre-processing and matching device behavior with patch applicability criteria, the system determines which devices actually need each patch before distribution, preventing unnecessary installations and reducing downtime.
Solution Approach 2:
The system collects feedback from user device behavior data to continuously improve patch matching accuracy. By monitoring actual device operations and comparing them against patch requirements, the system refines its ability to identify which devices need which patches, reducing both unnecessary installations and missed updates.
2Reliability
If software patches are pushed to all customers, then software defects can be addressed, but unnecessary patch installations increase computing costs
Solution Approach 1:
The system performs preliminary filtering and matching of patches to specific device behavior patterns before distribution. By pre-analyzing which devices require which patches based on their operational characteristics, the system avoids wasting computing resources on pushing and installing patches to devices that don't need them.
Solution Approach 2:
The system extracts and isolates only the specific patches needed for each device based on behavior analysis, rather than distributing all available patches to all devices. This selective extraction approach reduces unnecessary computing resource consumption on both server and client sides.
3Productivity
If traditional patch distribution methods are used, then patches can be delivered to customers, but installation failures occur due to lack of matching recommendations
Solution Approach 1:
The system performs preliminary matching of patches to device behavior patterns before installation. By analyzing device characteristics and comparing them against patch requirements in advance, the system ensures that only compatible patches are recommended for installation, significantly reducing installation failures.
Solution Approach 2:
The system introduces behavior pattern analysis as an intermediary layer between patch delivery and installation. This intermediary matching process acts as a filter that ensures compatibility between patches and specific device configurations, preventing mismatched installations.
Data Source
AI summary
Systems and methods are provided to recommend software patches based on task operation mapping. In embodiments, a method includes abstracting test cases for a software patch into a sequence of task operations and parameters associated with each task operation; encoding the task operations and the parameters associated with each task operation based on predetermined rules, thereby generating encoded task operations with unique identifiers assigned thereto and associated encoded parameters with numeric values assigned thereto; generating, using machine learning, a list of frequent operation items, based on the encoded task operations and the associated encoded parameters; generating, using clustering, clusters of parameters for each frequent operation item in the list of frequent operation items; and sending a software patch package including the list of frequent operation items, the clusters of parameters and the software patch to a remote server for distribution to one or more user devices.