Smart SDK Upgrade Module for Legacy Application Migration
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Solution Overview
Problem
The complexity of upgrading software applications to newer versions of programming language specifications, particularly due to non-backwards compatible changes, leads to difficulties in implementing security fixes and support, resulting in increased costs and manual work hours.
Innovation Solution
A platform and language agnostic smart SDK upgrade module is implemented, utilizing a dynamic machine learning model to automatically scan, detect, and execute upgrades, reducing the need for manual research and intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual upgrade processes are used for software applications, then control and accuracy over the upgrade process is maintained, but time consumption and labor costs increase significantly
Solution Approach 1:
The system enables self-service automation where the upgrade module automatically scans applications, identifies deprecated references, generates upgrade plans, and executes upgrades without requiring manual intervention from developers or operators
Solution Approach 2:
The system performs preliminary actions by scanning applications before upgrade to identify all deprecated references and generate comprehensive upgrade plans in advance, preparing everything needed for the actual upgrade execution
2Reliability
If applications are upgraded to newer programming language versions, then security fixes and modern features are accessed, but compatibility issues and complexity increase
Solution Approach 1:
The upgrade process is segmented into distinct phases: scanning phase to identify deprecated references, planning phase to generate upgrade strategies, and execution phase to perform actual replacements. This segmentation makes the complex upgrade process manageable and systematic
Solution Approach 2:
The system acts as an intermediary between the application code and the target programming language version, automatically translating and reconciling differences through intelligent reference replacement and dependency management
3Adaptability or versatility
If extended support models are maintained for legacy versions, then backward compatibility is ensured, but costs increase
Solution Approach 1:
The system changes the parameter of support approach from maintaining multiple legacy versions to enabling single-version upgrades, transforming the support model from horizontal (multiple versions) to vertical (version progression)
Data Source
AI summary
Various methods, apparatuses/systems, and media for automatically upgrading an application are disclosed. A processor creates a dynamic machine learning (ML) model; trains the dynamic ML model and scans for SDK upgrade for the application against the dynamic ML model by implementing ML algorithm for predictions. The processor executes the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to trigger the processor to perform the following automated processes: implement the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application; evaluate the predictive results data to determine whether there is a match for the deprecated reference; and when it is determined that there is a match for the deprecated reference, automatically replace code and upgrade the application to newer version of the programming language specification.