Automated Application Code Changes with Ranked Sandbox Testing
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Solution Overview
Problem
Large organizations face inefficiencies and unreliability in managing code changes across complex computing systems, leading to unintended consequences and potential system failures due to the interdependence of applications and computing devices.
Innovation Solution
A system and method for automatically selecting and testing replacement lines of code in a simulated environment, using a code repository with relevancy and safety rankings, to identify and implement code changes that meet organizational and regulatory requirements, thereby avoiding failures and ensuring smooth system operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated code selection and testing is implemented, then code deployment speed and system reliability are improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The system performs preliminary actions by pre-ranking code sets in a code repository based on relevancy and safety criteria before actual deployment is needed. When code changes are required, the system can quickly select from pre-evaluated options, significantly reducing deployment time while maintaining systematic rigor through automated testing in simulated environments.
Solution Approach 2:
The system introduces a simulated environment as an intermediary between code development and production deployment. This virtual sandbox allows automated testing of code changes without directly impacting the live system, enabling rapid iteration and selection of safe code changes while protecting system stability.
2Reliability
If manual code review processes are used, then system stability is maintained, but code update efficiency and speed decrease
Solution Approach 1:
The system implements automated feedback loops where code changes are automatically tested in simulated environments, and results are used to determine whether to proceed with deployment. This automated feedback mechanism maintains system stability through rigorous testing while dramatically improving code update efficiency by eliminating manual review bottlenecks.
Solution Approach 2:
The system creates virtual copies of the production environment in the simulated environment. These copies allow thorough testing of code changes without risking the actual production system, enabling rapid iteration and deployment decisions while maintaining production stability.
3Reliability
If extensive testing of code changes is performed, then unintended consequences are avoided, but testing time and deployment delays increase
Solution Approach 1:
The system applies different levels of testing rigor to different code sets based on their relevancy rankings. High-priority code changes with high relevancy scores undergo more extensive testing in the simulated environment, while lower-priority changes receive streamlined testing. This differentiated approach ensures thorough testing of critical changes while reducing testing time for less critical updates.
Solution Approach 2:
The system performs preliminary filtering and ranking of code sets before extensive testing. By pre-evaluating code changes based on relevancy and safety criteria, the system identifies which code sets require extensive testing and which can proceed with minimal testing, significantly reducing overall testing time while maintaining reliability for critical changes.
4Manufacturing precision
If multiple code sets are selected and tested iteratively, then the best code is identified, but the number of testing iterations and time required increase
Solution Approach 1:
The system performs preliminary ranking of code sets in the repository based on relevancy and safety criteria before iterative testing begins. This pre-sorting ensures that the most promising code sets are tested first, significantly reducing the number of iterations needed to identify the best code. The systematic ranking approach prevents random or haphazard selection, streamlining the iterative process.
Solution Approach 2:
The system changes the parameters used to evaluate code sets, specifically using relevancy and safety rankings as selection criteria. This parameter-based approach allows the system to systematically evaluate and compare multiple code sets, identifying the best code more quickly through quantitative metrics rather than subjective assessment, thereby reducing the number of iterations required.
Data Source
AI summary
A system for implementing code changes comprises a code repository that stores replacement lines of code that each have a relevancy ranking and a safety ranking. The system is configured to receive a notice from an external source hosting an application that a previous set of code associated with the application needs to be changed. The system then identifies a new set of codes from the code repository with high relevance and safety values. It implements a virtual secured environment that simulates the application with the identified new set of code and one or more other external source components. Feedback is then received from the virtual secured environment and recorded in a log. The new set of code is then sent to the external source for implementation when the feedback does not indicate a failure.