Legacy System Mapping via Screen Instance Clustering
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Solution Overview
Problem
Legacy systems are difficult to adapt due to unavailable source code and lack of expertise, making it challenging to understand and modify their functionality, and existing methods for analyzing these systems are incomplete, error-prone, and require significant manual intervention.
Innovation Solution
A method that generates a map of a legacy system by grouping screen instances based on pair-wise distance and similarity scores calculated from statistical content and structural analysis, using a stochastic approach to improve screen identification and grouping without manual interaction, and a system that records user trails to analyze communication between the legacy system and terminal without installing additional components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual screen identification methods are used, then developers can identify screens based on their knowledge, but the process is time-consuming and error-prone
Solution Approach 1:
The system performs automatic screen identification by analyzing trail data and computing similarity scores between screen instances, eliminating the need for manual developer intervention in screen identification while maintaining high accuracy through algorithmic comparison of screen characteristics
Solution Approach 2:
The patent replaces manual mechanical screen identification with an automated computational system that uses similarity scoring algorithms to automatically group screen instances into screen groups, substituting human developer effort with machine-based analysis
2Productivity
If simple screen identification rules are used, then the process is fast and automated, but the screen grouping is incomplete and requires significant manual intervention
Solution Approach 1:
The system uses similarity scores as feedback to iteratively improve screen grouping accuracy, comparing each screen instance against existing screen groups and adjusting group assignments based on computed similarity metrics, thereby achieving both automation and high completeness without manual intervention
Solution Approach 2:
The patent transforms screen identification from simple rule-based matching to a parameter-based similarity scoring system that evaluates multiple screen characteristics simultaneously, enabling more accurate and complete automated screen grouping by changing the identification parameters from binary rules to continuous similarity scores
3Loss of information
If additional components are installed on the mainframe for analysis, then detailed system information can be obtained, but the legacy system cannot be analyzed if components cannot be installed
Solution Approach 1:
The patent uses terminal emulator communication trails as an intermediary to access legacy system information without installing components on the mainframe itself, capturing screen data and user interactions through the existing terminal interface and using these trails for automatic screen identification and system mapping
Solution Approach 2:
The system creates copies of screen instances from terminal emulator communications and analyzes these copies to identify screens and build system maps, eliminating the need to install analysis components on the legacy system by working with replicated screen data instead
Data Source
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AI summary
The present invention concerns a method for analyzing a legacy system (20) based on at least one trail (100, 101), wherein the at least one trail (100, 101) comprises a plurality of instances (110) of screens of the legacy system (20) and wherein the method comprises the following steps: a. calculating a plurality of pair-wise distance measures of the plurality of screen instances (110) in the at least one trail (100, 101), wherein the pair-wise distance measures indicate a similarity of the respective pair of screen instances (110) that indicates that the pair may originate from the same screen of the legacy system (20); and b. applying at least one cluster analysis algorithm on the plurality of pair-wise distance measures to group the screen instances (110) into at least one screen group (200) and building a map (250) of the legacy system (20) based on the at least one screen group (200).