Code File Update via Signature Similarity Matching
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Solution Overview
Problem
Software updates often render existing code files incompatible, leading to productivity issues and errors, and manually identifying and updating these files is time-consuming and complex, especially in large enterprises, where the impact of updates on existing code files is unclear.
Innovation Solution
A system using pattern matching and machine learning to automatically identify and update code files by collecting updates from various sources, including within an organization or from crowd-sourced information, and applying updates to similar code files based on similarity thresholds, with optional manual review and feedback learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and updating of code files is performed, then update accuracy can be ensured, but time consumption and complexity increase significantly
Solution Approach 1:
The patent replaces manual mechanical identification and updating processes with an automated system that uses machine learning models and natural language processing to identify code files requiring updates and generate update content, thereby reducing time consumption while maintaining update accuracy
Solution Approach 2:
The system enables code files to be automatically updated through self-service mechanisms where the machine learning model autonomously identifies updates needed, generates update content, and applies changes without requiring manual intervention for each individual file
2Reliability
If comprehensive code file updates are performed across the organization, then compatibility with new software versions is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent segments the code file update process into distinct components: identification phase, content generation phase, and application phase. The system processes code files in groups or batches rather than all at once, managing complexity through modular architecture and staged implementation
Solution Approach 2:
The machine learning model serves as an intermediary between the software update and the code files, translating update requirements into specific code modifications. This intermediary layer simplifies the overall system by handling the complex transformation logic centrally
3Productivity
If automatic update application is implemented without manual review, then productivity is improved, but risk of errors increases
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model learns from the performance and outcomes of previous updates. The system monitors update results, identifies patterns in successful versus unsuccessful updates, and continuously refines its algorithms to reduce error rates while maintaining high productivity
Solution Approach 2:
The system performs preliminary analysis and validation of code files before applying updates automatically. The machine learning model pre-assesses which files are suitable for automatic updating and prepares update content in advance, reducing the risk of errors during actual application
Data Source
AI summary
Methods and systems for updating a file using heuristics. One system includes an electronic processor configured to identify a code file stored on a storage device and determine a signature of the code file. The electronic processor is also configured to compare the signature of the code file to each of a plurality of signatures to determine a degree of similarity between the signature of the code file and each of the plurality of signatures, wherein each of the plurality of signatures is associated with a code update, and, in response to the degree of similarity between the signature of the code file and one of the plurality of signatures satisfying a predetermined threshold, apply the code update associated with the one of the plurality of signatures to the code file.