Source Code Scanning for Database Migration
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Solution Overview
Problem
Current database migration tools lack the ability to efficiently identify and revise mixed language applications accessing data in source and destination databases, as they require manual analysis of all source code files, which is impractical.
Innovation Solution
A framework and toolset for creating migration rules that filter and scan source code files to locate and automatically replace artifact expressions, using recognizer criteria and replacement rules tailored to specific database and language combinations, improving performance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all source code files are manually analyzed to identify database calls, then completeness of identification is improved, but time consumption and complexity increase significantly
Solution Approach 1:
The patent segments the source code analysis by creating a subset of files that are likely to contain database calls, rather than analyzing all files. This is achieved through filtering mechanisms that divide the complete codebase into relevant and irrelevant portions, reducing analysis time while maintaining identification completeness for the critical subset.
Solution Approach 2:
The patent introduces an intermediary filtering layer between the complete source code and the analysis process. This intermediary component evaluates files against criteria to determine which ones should be scanned, acting as a mediator that reduces the workload of the main analysis engine while ensuring relevant files are not missed.
2Measurement precision
If all source code files are scanned to ensure no database calls are missed, then identification accuracy is improved, but processing speed deteriorates
Solution Approach 1:
The source code base is segmented into a subset of files likely to contain database calls using filtering criteria. This segmentation allows the scanning process to focus only on relevant files, maintaining identification accuracy for database calls while dramatically improving processing speed by excluding irrelevant files from the scanning process.
Solution Approach 2:
Instead of scanning all files (excessive action), the patent applies partial action by scanning only the subset of files identified as likely to contain database calls. This partial scanning approach maintains sufficient identification accuracy for migration purposes while significantly reducing processing time and resource consumption.
3Manufacturing precision
If manual revision of database calls is performed, then precision of revision is improved, but labor cost and time increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically identify and revise database calls in source code files. The tool autonomously performs the revision process using identified migration rules, eliminating or reducing the need for manual human intervention while maintaining precision through systematic application of migration patterns.
Solution Approach 2:
The patent substitutes the mechanical manual process of reviewing and revising database calls with an automated computational system. This replacement uses algorithmic patterns and rules to automatically detect and revise database calls, reducing labor costs while maintaining or improving revision precision through consistent rule application.
Data Source
AI summary
Systems, methods, and other embodiments associated with application source code scanning for database migration are described. In one embodiment, a method includes identifying a subset of application source code files that are likely to require modification to access a destination database. The subset of application source code files is scanned for artifact expressions that access a source database and should be changed to access the destination database.