Automated Library Recommendation Engine for Code Quality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing software development tools face challenges in efficiently adding and managing library functions due to the difficulty in identifying, uploading, and modifying libraries, leading to sub-optimal code quality and performance.
Innovation Solution
A system and methodology that automates the addition of library functions to a library recommendation engine using YAML configuration files, which are validated and populated automatically, leveraging machine learning, NLP, and AI to suggest library substitutions and improvements in source code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If programmers manually identify and integrate libraries from thousands of stored library files, then code quality and reliability can be improved through library usage, but the effort, knowledge, and experience required increases significantly
Solution Approach 1:
The system enables automatic self-service by allowing the library recommendation engine to autonomously identify, validate, and integrate libraries without requiring programmer intervention. The engine automatically scrapes documentation pages, extracts test cases, generates configuration files, and validates library functions, transforming a manual complex process into an automated self-service system.
Solution Approach 2:
The patent replaces the mechanical manual process of library identification and integration with an automated computational system. The library recommendation engine uses automated scraping, extraction, validation, and integration mechanisms to substitute the manual mechanical actions of programmers searching through thousands of library files, thereby reducing effort while maintaining code quality.
2Ease of operation
If programmers manually upload and modify library functions, then library usage can be tracked and enforced, but the responsibility and difficulty of correctly identifying and integrating libraries remains with the programmer
Solution Approach 1:
The system performs preliminary actions by automatically scraping documentation pages, extracting test cases, and generating configuration files before the library integration process. This preliminary automated preparation eliminates the time-consuming manual work of identifying and preparing library functions, allowing programmers to directly integrate pre-validated libraries.
Solution Approach 2:
The library recommendation engine acts as an intermediary between the vast library repository and the programmer. It automatically searches through thousands of library files, validates functions, generates configuration files, and presents ready-to-integrate libraries to programmers, thereby simplifying the integration process and reducing the time required for library identification.
3Reliability
If manual control procedures are used for reviewing and validating proposed library additions, then library knowledge base integrity can be maintained, but the process becomes extremely difficult and time-consuming
Solution Approach 1:
The patent replaces manual control procedures with automated computational validation mechanisms. The library recommendation engine automatically validates proposed library additions by scraping documentation, extracting and executing test cases, and generating configuration files, thereby maintaining library knowledge base integrity while dramatically increasing the speed of library addition.
Solution Approach 2:
The system enables self-service validation where the library recommendation engine autonomously reviews and validates proposed library additions without requiring manual control procedures. The engine automatically executes test cases, validates function signatures, and ensures compatibility with the existing library knowledge base, maintaining integrity while improving productivity.
Data Source
AI summary
A method, system, and apparatus are disclosed for adding library models to a library knowledge base by defining a template for a library configuration file that conveys information about each library model, custom inputs and code snippets to facilitate library comparison operations, and education content for the library model, where the library configuration file template may be automatically filled by populating selected data fields in the template with information identifying the library model, scraping documentation pages to extract test cases, and then scraping test case code to extract the test case input parameters for input to an input/output matching engine to evaluate a repository of code snippets and identify a set of functionally similar code snippets for inclusion one or more data fields in the template.