Code Package Version Scoring for Reliable Repository Selection

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Solution Overview

Problem

Software repositories face issues with untested code packages being uploaded, leading to bugs, security vulnerabilities, and inefficiencies due to developers defaulting to subpar versions, causing latency and increased memory overhead.

Innovation Solution

Implementing a package repository that automatically tests code packages using a machine learning model to assess and score versions, rejecting defective packages and recommending the best version based on test results.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If developers upload code packages without automatic testing, then the upload process is simple and fast, but bugs and security vulnerabilities are introduced into the repository

Engineering Contradiction:

Improveupload speedVSAvoidcode quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs automatic testing and evaluation of code packages before they are fully integrated into the repository. A machine learning model pre-assesses packages for bugs, security vulnerabilities, and quality metrics, allowing problematic packages to be identified and rejected before deployment, thus maintaining both speed and reliability

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The code packages automatically undergo testing and evaluation by the machine learning model without requiring manual review. The system self-services by autonomously assessing package quality, identifying issues, and making acceptance decisions, eliminating the need for human intervention while maintaining high standards

Inventive Principle:

Principle #25Self-service

2Ease of operation

If developers default to latest version of code packages, then version selection is simple, but latency and memory overhead increase due to subpar versions

Engineering Contradiction:

Improveversion selectionVSAvoidlatency

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The machine learning model provides feedback scores for different package versions based on performance metrics, bug presence, and security assessments. This feedback guides developers to select optimized versions rather than defaulting to the latest version, reducing latency and memory overhead while keeping the selection process simple through automated recommendations

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system evaluates and ranks package versions based on multiple parameters including execution speed, memory usage, bug count, and security score. By changing the selection criterion from 'latest version' to 'highest quality score', the system automatically identifies and recommends versions that optimize performance without increasing complexity for the developer

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If all versions of code packages are stored in the repository, then version availability is high, but memory overhead increases

Engineering Contradiction:

Improveversion availabilityVSAvoidmemory overhead

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The machine learning model extracts and identifies subpar versions of code packages that meet quality thresholds. These low-quality versions are then removed or marked for rejection from the repository, retaining only versions that pass the automated assessment. This maintains version availability for quality packages while reducing storage of defective versions

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system discards code package versions that fail quality assessments based on machine learning evaluation. By automatically identifying and removing subpar versions while preserving validated versions, the repository maintains adaptability for legitimate version selection while minimizing memory overhead from storing known-defective packages

Inventive Principle:

Principle #34Discarding and recovering

Data Source

PatentUS20260030018A1Machine learning for package versioning and use

Publication Date: 2026.01.29 CAPITAL ONE SERVICES LLC

AI summary

In some implementations, a user device may transmit, to the package repository, a request for a code package. The user device may receive, from the package repository, an indication of a plurality of versions of the code package. The user device may receive, from the package repository, an indication of a suggested version from the plurality of versions. The suggested version is based on automatic testing of the code package by the package repository. The user device may transmit, to the package repository, a request for the suggested version. The user device may receive, from the package repository, at least one file included in the suggested version.