Source Code Commitment Correlation for Model Development
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Solution Overview
Problem
Conventional systems for managing source code commitments and model results in software development rely on external software, limiting contributor control over storage and monitoring, leading to inefficiencies and increased risk of errors and data loss.
Innovation Solution
The system allows contributors to manage source code commitments and model result records using native source code commands and libraries, enabling control over storage location and correlation without external software, thereby enhancing efficiency and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional external software systems are used to monitor and store model commitments and results, then automatic monitoring and storage is achieved, but contributor control over storage location and monitoring mechanisms is lost
Solution Approach 1:
The system enables contributors to self-manage their commitments and results by embedding storage control directly in the source code. Contributors can specify storage locations and monitoring mechanisms through native code commands, eliminating the need for external software while maintaining full control over their data.
Solution Approach 2:
The patent merges the commitment tracking and result storage functions into the source code itself rather than using separate external software systems. This integration allows contributors to control both monitoring and storage through unified native commands, resolving the contradiction between automation and control.
2Extent of automation
If external software systems automatically store all commitments, then comprehensive monitoring is achieved, but the system becomes too complex and results in a mass of updates that are too numerous to effectively review
Solution Approach 1:
The system segments the storage process by allowing contributors to selectively store only relevant commitments and results through native source code commands. This segmentation prevents the creation of a mass of unnecessary updates while maintaining the ability to monitor important changes automatically.
Solution Approach 2:
The patent implements local quality by allowing each contributor to define their own storage and monitoring preferences in their specific code. This enables tailored monitoring that focuses on relevant commitments rather than uniformly processing all commitments, reducing overall system complexity.
3Extent of automation
If conventional external software is used for commitment management, then centralized control is achieved, but the risk of system errors and crashes increases
Solution Approach 1:
The patent extracts the commitment management functionality from external software and embeds it directly in the source code. This extraction eliminates the intermediary layer that caused system errors and crashes, while contributors can still maintain centralized control through the integrated system.
Solution Approach 2:
The native source code commands and libraries serve as an intermediary layer between contributors and the storage system. This intermediary is built into the code itself rather than being a separate external software, eliminating the reliability issues associated with external software while maintaining centralized control capabilities.
4Ease of operation
If contributors manually record data about commitments, then control over storage is maintained, but productivity decreases due to the time required for manual recording
Solution Approach 1:
The system performs preliminary action by automatically executing storage and monitoring operations through embedded native commands in the source code. Contributors define their storage preferences in advance through code, and the system automatically executes these commands without requiring manual data recording during development.
Solution Approach 2:
The patent replaces the mechanical manual recording process with automated native source code commands. Contributors control storage through code rather than manual operations, and the system automatically executes these commands, eliminating the time-consuming manual recording step while maintaining full control.
Data Source
AI summary
Methods and systems are described herein for improvements to model development, particularly in terms of source code commitments for these models. For example, methods and systems allow contributors to manage when and where commitments and/or the results of those commitments are stored and/or do so without the use of an additional layer of external software by correlating source code commitments and model result records during model development. Moreover, the methods and systems allow contributors to manage when and where commitments are store automatically without the use of an additional layer of external software through user of native source code commands and libraries.