SYSTEMS AND METHODS FOR INTELLIGENT RETRIEVAL OF SOURCE CODE

Smart overview of the Invention

The patent application outlines a system designed to enhance the retrieval and reuse of source code using artificial intelligence. It introduces a search engine that helps users locate and retrieve relevant code segments efficiently. The system's context-based search functionality tailors code retrieval to specific user requirements, improving the relevance of results. Additionally, it incorporates user reviews and single-click downloads to facilitate easy code discovery and integration. The system continuously tracks metrics like views, downloads, and ratings to maintain high-quality results.

Background

Software engineers often rely on external code resources to speed up development. However, using unvetted code can introduce security risks and unreliable elements into applications. The application addresses these concerns by offering a method to intelligently search and assess source code quality, reducing potential vulnerabilities and enhancing code reliability.

Key Features

The system allows users to submit a query with search terms, which are parsed by a language analyzer to generate input keywords. These keywords help identify code segments from a private database, which are then assessed for quality. The resulting code segments are ranked based on various quality metrics, such as standard compliance and security scores. Users can view and download these segments through a user-friendly interface, which may also include an option to query public databases using large language models.

System Components

The system comprises a memory and processor setup that processes queries and retrieves code segments based on metadata matching. It includes features like a quality assessment system that evaluates code segments for potential bugs and user ratings. The system supports querying both private and public databases, integrating results from public sources using advanced language models. Users can also submit new code segments, which are stored with metadata in the private database.

Operational Flow

The operational flow involves users entering search queries into an interface, which are then parsed by a language analyzer to produce keywords. These keywords are matched against a private database to retrieve relevant code segments. The system can also query public databases to expand search results. Users can filter and organize the results based on quality and compatibility metrics, ensuring they find the most suitable code for their needs. The system's feedback-driven approach helps refine search operations over time.