Description:FIELD OF THE INVENTION
The present invention relates to a system and method for fine-tuning large language models (LLMs) to automate the process of upgrading open-source libraries and APIs. The invention addresses the challenges associated with identifying deprecated APIs, understanding their replacements, and updating code efficiently by introducing a systematic framework.
BACKGROUND OF THE INVENTION
With the growing reliance on open-source libraries in various domains, the process of maintaining and upgrading these libraries has become increasingly critical. Upgrades often involve addressing deprecated elements, adapting to new functionalities, and ensuring compatibility across versions. Traditionally, these upgrades are performed manually, requiring individuals to identify deprecated components, analyze their replacements, and implement changes. This process is labor-intensive, error-prone, and time-consuming, especially in complex systems with intricate dependencies.
Existing approaches to automating such upgrades often lack a systematic and reliable methodology. They fail to address the need for generating accurate training data, validating changes comprehensively, and ensuring functional consistency between previous and upgraded versions. As a result, the process remains fragmented, inefficient, and prone to introducing discrepancies.
There is an increasing need for systems that can seamlessly automate these upgrades with precision and scalability while minimizing human intervention. Existing solutions do not provide a holistic framework that combines data collection, validation, and modification techniques to ensure seamless transitions between versions. Without such a system, the process of upgrading open-source libraries remains inefficient, consuming valuable time and resources.
Prior attempts to address fine-tuning challenges have introduced innovative approaches but remain incomplete in addressing continuous updates, contextual relevance, and data security within private deployments.
For instance, US20190243617A1 describes a system for enhancing code development by utilizing machine learning models to assess changes in a codebase and assist with code editing and debugging. While it offers improvements in code editing and generation, this invention primarily focuses on static training and manual code enhancements, and does not support real-time dynamic updates or the continuous integration of data from constantly evolving open-source libraries. It also lacks a comprehensive mechanism for ensuring the contextual relevance of the code being modified, making it unsuitable for real-time adaptations required in rapidly changing software environments like open-source library upgrades. Additionally, it does not address the challenges of ensuring data privacy and security in private organizational settings, a crucial aspect when deploying such systems in sensitive environments.
Similarly, US20240020096A1 presents a method for generating code using machine learning models that interpret natural language inputs and produce code samples. While this invention is effective in generating code based on pre-defined specifications, it does not offer a solution for continuous fine-tuning of language models in the context of real-time updates to open-source libraries or for automating open-source upgrades. The system does not incorporate mechanisms for handling deprecated code or ensuring data security and privacy while training models in private environments. Moreover, it lacks the ability to integrate evolving domain-specific data into the model fine-tuning process, rendering it inadequate for ongoing model adaptation.
Similarly, US20220244937A1 outlines a system for automating software code modification using machine learning models that process requirement data to modify software code. While this system supports automated code modification, it is limited in that it focuses on general code transformation rather than automating the upgrade process for deprecated APIs and open-source library changes. It also does not include the ability for continuous fine-tuning or dynamic adaptation to new organizational data, nor does it ensure that the generated code remains contextually relevant to the specific needs of an organization. Additionally, there is no mention of integrating privacy and security measures for private deployments, which is a key concern in real-world organizational settings.
Lastly, US11604628B2 discusses tools for automating software development aspects, including generating recommendations based on prior code transformations. However, this approach fails to address automated open-source upgrades or real-time fine-tuning of language models for integrating ongoing organizational data. The invention also lacks a comprehensive mechanism for ensuring contextual relevance during model adaptation or security of training data, which is essential in private environments with sensitive organizational data.
In contrast, the present invention introduces a novel system and method that integrates dynamic organizational data into language models continuously, ensuring real-time updates and maintaining contextual relevance while securing the data used for model fine-tuning. By utilizing modules like a data bus, context-graph, and fine-tuning pipeline, the present invention provides a continuous, secure, and contextually accurate solution for open-source upgrades and model adaptation, overcoming the limitations of prior art in supporting real-time organizational needs.
DEFINITIONS
"Libraries" refers to collections of pre-written code or components, including APIs, frameworks, and modules, which provide reusable functionalities for various applications. These libraries are often maintained and updated by open-source communities.
"Deprecated Elements” refers to outdated or obsolete components within a library, such as APIs or methods, that have been replaced or removed in newer versions due to improved functionality or design changes.
"Abstract Syntax Tree (AST)” refers to a tree-like data structure used to represent the syntactic structure of code. It facilitates analysis, modification, and validation of source code for upgrading deprecated elements.
"API Compatibility" refers to the ability of different versions of a library to interact seamlessly by maintaining functional consistency and minimizing disruptions caused by changes in the library's APIs.
"Knowledge Base" refers to a centralized repository that documents information about libraries, including API changes, deprecations, replacements, and usage recommendations across different versions.
"Validation Mechanism" refers to the processes and techniques used to ensure the correctness, functionality, and compatibility of code after upgrading deprecated elements.
"Training Examples" refers to paired data consisting of pre-upgrade code (using deprecated elements) and post-upgrade code (with updated replacements) used to fine-tune models for automating the upgrade process.
"Reinforcement Learning" refers to a machine learning approach employed to improve validation processes by prioritizing test cases that are more likely to detect errors in upgraded code.
"In-Place Code Injection" refers to the method of programmatically modifying source code directly at the location of deprecated elements, ensuring minimal changes while maintaining functionality.
"Delta Version Analysis" refers to the process of comparing consecutive versions of a library to identify changes, including additions, modifications, and deprecations, and to map their impact on dependent code.
"Code Upgrade Pipeline" refers to a structured sequence of processes that automate the collection, analysis, modification, and validation of source code for upgrading open-source libraries.
"Feedback Loop" refers to an iterative process where the results of validation and performance analysis are used to refine and enhance the system’s ability to handle upgrades effectively.
"Compatibility Graph" refers to a graph-based representation of the relationships and dependencies among APIs, library versions, and their compatibility attributes, aiding in precise analysis of changes.
"Stratified Sampling" refers to a method of selecting diverse and representative training examples by categorizing code snippets based on their usage patterns and characteristics.
"Functional Equivalence" refers to the state where pre-upgrade and post-upgrade code perform identical operations and produce consistent results, ensuring correctness after the upgrade.
OBJECTS OF THE INVENTION
The primary objective of the invention is to provide a system and method for automating the process of upgrading open-source libraries by leveraging advanced analytical techniques and fine-tuning large language models (LLMs) to ensure accuracy and efficiency.
Another objective of the invention is to provide a system and method for systematically identifying and validating deprecated components, ensuring seamless replacement with updated functionalities while maintaining compatibility across versions.
A further objective of the invention is to provide a system and method to generate high-quality training examples by pairing pre-upgrade and post-upgrade code, facilitating effective fine-tuning of models for upgrade automation.
Yet another objective of the invention is to provide a system and method for incorporating a feedback-driven validation process to refine model performance and improve the accuracy of automated upgrades over time.
An additional objective of the invention is to implement a comprehensive knowledge base and compatibility graph to document and analyze library versions, API changes, and their dependencies, enabling precise and reliable upgrades.
A final objective of the invention is to ensure scalability and adaptability of the system to handle upgrades for diverse libraries, programming languages, and application domains, ensuring consistent performance and minimal disruption to ongoing processes.
SUMMARY OF THE INVENTION
Before the present invention is described, it is to be understood that the present invention is not limited to specific methodologies and materials described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention.
The present invention discloses a system and method for evaluating and routing language models (LLMs) for product and application development within organizational environments. This system addresses the challenges of ensuring optimal model selection, task routing, and output relevance across diverse organizational use cases. Central to the system is an analyze request module, which evaluates incoming tasks based on attributes like task type, urgency, and domain-specific requirements. The system utilizes a compute scores module to calculate task suitability, ranking available language models based on their ability to address specific tasks.
The select model module then chooses the most appropriate LLM based on computed scores, ensuring efficient task handling. Once selected, the route task module directs the task to the chosen model, optimizing task processing. The generate output module ensures that the output generated is contextually accurate and relevant to the task at hand. A key feature of the system is the evaluate feedback module, which assesses the generated output for accuracy, quality, and relevance, enabling continual model optimization.
Additionally, the system incorporates an update parameters module that continuously updates and fine-tunes the model’s parameters based on feedback and task performance, ensuring sustained output accuracy over time. The system’s architecture is designed to handle diverse task requirements, offering scalable solutions for product development and application design within an organizational setting.
This invention significantly improves the efficiency and accuracy of language models by enabling real-time evaluation, model selection, task routing, and continuous fine-tuning, providing organizations with a robust solution for deploying intelligent models in dynamic, real-world environments.
DETAILED DESCRIPTION OF THE INVENTION
Before the present invention is described, it is to be understood that this invention is not limited to methodologies described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention. Throughout this specification, the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the invention to achieve one or more of the desired objects or results. Various embodiments of the present invention are described below. It is, however, noted that the present invention is not limited to these embodiments, but rather the intention is that modifications that are apparent are also included.

The present invention relates to a comprehensive system for automating the process of upgrading open-source libraries and ensuring compatibility across versions. This system is designed to systematically identify deprecated elements, generate replacements, and validate the upgraded code to maintain functionality and accuracy. By utilizing advanced methodologies, the invention ensures that upgrades are performed efficiently with minimal manual intervention.

The system comprises a structured sequence of interconnected modules, each responsible for specific stages of the upgrade process. These modules collectively work to analyze libraries, collect data, generate upgraded code, and validate the changes, ensuring accurate and reliable outcomes.

The system includes several key components: Collect Libraries Module, Analyze API Compatibility Module, Validate Data Module, Collect Deprecated Code Module, Generate Upgraded Code Module, Validate Code Output Module, Create Training Data Module, and concludes with the End Module.

Collect Libraries Module
The collect libraries module serves as the initial intake point for gathering data. It systematically collects all versions of relevant open-source libraries and their metadata. This module ensures that the system has access to comprehensive and accurate library data for subsequent analysis.By leveraging automated scripts and filters, the module extracts library versions, prioritizes stable releases, and stores the data for further processing. This step ensures that only high-quality and relevant data is retained for analysis.

Analyze API Compatibility Module
The analyze API compatibility module identifies and evaluates changes in APIs across library versions. It uses techniques such as dependency graph analysis and version delta comparisons to map the lifecycle of APIs, including creation, modification, and deprecation. This module ensures that the system has a clear understanding of API compatibility and its impact on dependent code, enabling accurate identification of necessary upgrades.

Validate Data Module
The validate data module ensures the accuracy and reliability of collected information. Using a combination of heuristic rules and validation through trained models, this module cross-references and verifies API changes and recommendations. By integrating feedback loops, the module improves data quality iteratively, ensuring robust and reliable inputs for the upgrade process.

Collect Deprecated Code Module
The collect deprecated code module focuses on identifying and gathering instances of deprecated code from various repositories. It employs advanced search algorithms to locate code snippets that rely on outdated APIs. This module clusters similar patterns of deprecated code for efficient processing, ensuring comprehensive coverage of upgrade scenarios.

Generate Upgraded Code Module
The generate upgraded code module creates updated versions of code snippets by replacing deprecated elements with their appropriate alternatives. It uses Abstract Syntax Tree (AST) methods and learned models to perform in-place code modifications. This module ensures that minimal changes are made to the original code while maintaining its functionality, providing precise and reliable upgrades.

Validate Code Output Module
The validate code output module ensures the correctness and equivalence of the upgraded code. It runs both the pre-upgrade and post-upgrade code through automated validation pipelines, using test cases and runtime checks to confirm that functionality remains consistent. By prioritizing error-prone scenarios using reinforcement learning, the module ensures a thorough validation process that minimizes defects.

Create Training Data Module
The create training data module generates high-quality training datasets by pairing pre-upgrade and post-upgrade code snippets. These datasets are optimized using stratified sampling techniques to ensure diverse and representative examples for fine-tuning models. This module plays a critical role in improving the accuracy and adaptability of the system over time.

End Module
The end module marks the conclusion of the upgrade process. Once all steps are completed, this module ensures that outputs are finalized, validated, and delivered. The module prepares the system for new tasks, maintaining readiness and efficiency for future operations.

While considerable emphasis has been placed herein on the specific elements of the preferred embodiment, it will be appreciated that many alterations can be made and that many modifications can be made in preferred embodiment without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.
, Claims:We claim,
1. A system for fine-tuning language models for automating open-source upgrades
characterized in that
the system comprises of
a web scraper module configured to collect versions and metadata of open-source libraries; an LLM filtering module that refines and filters irrelevant data, ensuring only high-quality libraries are retained; a graph-based dependency analysis module that models API relationships and compatibility across versions; a knowledge base module documenting API lifecycles and providing recommendations for deprecated versions; a data validation module that integrates LLM feedback loops to ensure the accuracy of collected data; a pre-upgrade code collection module that identifies deprecated code in repositories using search algorithms; an upgraded code generation module that utilizes AST-based techniques to produce upgraded code based on the knowledge base and LLM interpreter suggestions; a validation pipeline module that employs automated test cases to confirm equivalence between pre-upgrade and upgraded code; a training example creation module that pairs pre-upgrade and post-upgrade code as training data for fine-tuning the LLM;
and the method comprises the steps of:
a. collecting open-source library versions and metadata using automated scraping techniques;
b. filtering the collected data using an LLM to ensure high-quality, relevant libraries are retained;
c. analyzing API compatibility across versions using a graph-based dependency analysis method;
d. documenting the API lifecycle and identifying deprecated versions in a knowledge base;
e. validating the collected data using an LLM feedback loop and heuristic rules;
f. collecting deprecated code samples from repositories using advanced search algorithms;
g. generating upgraded code based on LLM suggestions and AST-based techniques;
h. validating the upgraded code using automated test cases and reinforcement learning models;
i. creating training examples by pairing pre-upgrade and post-upgrade code.
2. The system and method as claimed in claim 1, wherein the web scraper module uses automated scripts to extract library versions and metadata from official sites of programming languages.
3. The system and method as claimed in claim 1, wherein the graph-based dependency analysis module creates a directed graph with nodes representing APIs and edges representing compatibility or dependencies between them.
4. The system and method as claimed in claim 1, wherein the knowledge base module includes machine learning techniques to predict future compatibility trends based on historical API data.
5. The system and method as claimed in claim 1, wherein the data validation module cross-references LLM feedback with heuristic rules to ensure the reliability of API compatibility information.
6. The system and method as claimed in claim 1, wherein the pre-upgrade code collection module uses advanced search algorithms to identify deprecated APIs in millions of code repositories.
7. The system and method as claimed in claim 1, wherein the upgraded code generation module applies Abstract Syntax Tree (AST) diffing algorithms to ensure minimal changes to the code while maintaining functionality.
8. The system and method as claimed in claim 1, wherein the validation pipeline module uses reinforcement learning models to prioritize test cases that are most likely to identify errors in the upgraded code.
9. The system and method as claimed in claim 1, wherein the step of collecting open-source library versions and metadata further involves filtering the libraries to retain only major, stable releases.