Automated Source-Code Library Upgrades for Security and Compliance
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Solution Overview
Problem
Existing software development tools struggle to efficiently identify and upgrade libraries in source code due to the difficulty in accurately and efficiently identifying performance limitations such as security vulnerabilities, license constraints, and compliance policy issues, often resulting in manual corrections and numerous false positives.
Innovation Solution
A system and method that uses machine learning, natural language processing, and artificial intelligence to automatically analyze libraries in source code, identify problematic libraries, and suggest suitable alternatives, then modifies the source code to incorporate these alternatives, thereby resolving performance limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing tools are used to identify library issues, then security vulnerabilities and compliance issues can be detected, but manual corrections are required and the process is time-consuming
Solution Approach 1:
The system enables self-service by automatically performing library identification, issue detection, alternative selection, and code modification without requiring manual intervention from developers. The automated dependency analysis tool independently completes the entire upgrade process, allowing the system to serve itself rather than requiring human operators to manually correct issues.
Solution Approach 2:
The patent replaces the mechanical manual process of reviewing and correcting library issues with an automated computer-based system. Instead of developers manually checking dependencies and editing code, the system uses automated algorithms, machine learning models, and code analysis tools to detect issues and apply fixes, substituting human mechanical work with automated digital processes.
2Productivity
If libraries are used to improve code reuse, then productivity increases, but difficulty in identifying suitable libraries and understanding their requirements increases complexity
Solution Approach 1:
The system introduces an intermediary automated dependency analysis tool that mediates between developers and the vast library ecosystem. This intermediary layer automatically handles the complex tasks of identifying suitable libraries, analyzing their requirements, checking compatibility, and managing dependencies, thereby simplifying the interaction for developers while maintaining high productivity benefits.
Solution Approach 2:
The automated tool provides multi-functional capabilities including dependency analysis, vulnerability detection, compliance checking, alternative identification, and automatic code modification. By consolidating these multiple functions into a single universal system, the patent reduces the overall complexity that developers would face if using separate tools for each function.
3Reliability
If existing library identification tools are used, then security issues can be flagged, but false positives occur and results become non-actionable when large numbers of files are identified
Solution Approach 1:
The system incorporates feedback mechanisms that continuously refine its identification accuracy. By analyzing the context of library usage, tracking actual code execution patterns, and learning from developer actions, the system adjusts its detection algorithms to reduce false positives. The feedback loop ensures that identified issues are increasingly accurate and actionable over time.
Solution Approach 2:
Instead of applying uniform detection rules across all codebase elements, the system applies localized analysis that considers the specific context, usage patterns, and importance of each library and code region. This local quality approach allows the system to distinguish between truly problematic libraries and false positives by evaluating each case individually based on its specific characteristics and impact.
4Manufacturing precision
If manual library upgrades are performed, then precision in selecting alternatives can be maintained, but the process becomes labor-intensive and slow
Solution Approach 1:
The patent replaces the manual mechanical process of reviewing and selecting library alternatives with automated computer-based analysis. The system uses machine learning models, code analysis algorithms, and automated code generation to select appropriate alternatives and perform upgrades, dramatically increasing speed while maintaining precision through sophisticated automated decision-making processes.
Solution Approach 2:
The system performs preliminary analysis and preparation before the actual upgrade process. By pre-analyzing code dependencies, pre-identifying suitable alternatives, pre-checking compatibility requirements, and pre-preparing modification scripts, the system enables rapid execution of upgrades with high accuracy, eliminating the need for slow manual review during the actual upgrade process.
Data Source
AI summary
A method and apparatus are disclosed for upgrading libraries in a source code program by evaluating libraries in the source code program for predetermined selection criteria specifying library performance limitations to identify at least a first library which does not meet the plurality of predetermined selection criteria and then identifying a first alternative library that is suitable for substitution for the first library so that the source code program may be automatically modified to replace the first library with the first alternative library, thereby generating a modified source code program having an upgraded library functionality.