Code Clustering and Transformation for ERP Version Upgrades
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Upgrading ERP systems with customized code objects is challenging due to incompatibilities between old and new versions, requiring extensive manual rewriting that is time-consuming and expensive.
Innovation Solution
A system and method for automatically identifying, analyzing, and transforming customized code objects from a source to a target ERP installation using a meta-model and transformation rules, with automatic, semi-automatic, or manual options for upgrading custom interfaces and database schema.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rewriting of customized code objects is performed during ERP system upgrade, then compatibility with new version is achieved, but time consumption and cost increase significantly
Solution Approach 1:
The system creates a copy of the customized code objects from the source system and applies transformations to the copy rather than manually rewriting each object. The transformation engine copies the structure and customization logic while adapting it to the target system version, significantly reducing the time required compared to manual rewriting.
Solution Approach 2:
A transformation engine acts as an intermediary between the source system's customized code objects and the target system. This intermediary automatically analyzes the customized objects, determines compatibility issues, and generates transformed versions that work with the new ERP version, eliminating the need for extensive manual intervention.
2Reliability
If manual rewriting of customized code objects is performed during ERP system upgrade, then compatibility with new version is achieved, but cost increases significantly
Solution Approach 1:
The transformation engine performs self-service by automatically analyzing customized code objects, identifying compatibility issues, and generating transformed versions without requiring extensive manual programmer intervention. The system serves itself by using built-in transformation rules and algorithms to handle the upgrade process, reducing labor costs significantly.
Solution Approach 2:
Instead of manual rewriting, the system copies customized code objects and applies automated transformations to create compatible versions for the target system. This copying approach with automated processing reduces the expensive manual labor required for traditional upgrades.
3Productivity
If all customized code objects are transformed automatically, then upgrade speed increases, but transformation accuracy may decrease
Solution Approach 1:
The transformation process is segmented into different categories: fully automatic transformation for standard objects, semi-automatic transformation for objects requiring partial manual review, and manual transformation for complex custom objects. This segmentation allows the system to maintain high speed for routine transformations while ensuring accuracy for critical objects through selective human review.
Solution Approach 2:
The system implements feedback mechanisms where transformation results are validated against the target system requirements, and any issues are fed back to the transformation engine for correction. This feedback loop ensures that automated transformations maintain high accuracy by continuously verifying and adjusting the transformed objects.
Data Source
AI summary
The present application is directed towards systems and methods for cluster-based code analysis and transformation. Cluster-based analysis may group code objects based on their similarity across functional areas, such as where a code object is cloned in multiple areas (e.g. sort functions that are duplicated across areas, or reports or tables that are identical). In some implementations, objects may be grouped into clusters by type, or based on reading from or writing to a common table. In some implementations, clustering at different layers may be possible.