LLM Pipeline for Declarative Deployment Code Quality

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Solution Overview

Problem

Developers face challenges in ensuring the quality of declarative deployment code for cloud-native applications due to the complexity of modern cloud frameworks and varying expertise levels within development teams, which hinders adherence to best practices and design patterns, and existing static analysis tools are insufficient in addressing these issues.

Innovation Solution

A machine learning pipeline utilizing a Large Language Model (LLM) is implemented to extract features from manifest files, cluster and label the data, and provide quality recommendations, including classification, issue identification, and design pattern analysis, to automate quality assurance and improve deployment practices.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual code review and knowledge transfer are used to ensure deployment code quality, then quality assurance is improved, but time consumption and complexity increase

Engineering Contradiction:

Improvedeployment code qualityVSAvoidtime for code review and knowledge transfer

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual code review (mechanical human process) with an automated machine learning system that uses large language models to analyze deployment manifests. The system extracts features from manifests, clusters them using unsupervised learning, and generates quality recommendations automatically, eliminating the time-consuming manual review process while maintaining quality assurance.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent implements a self-service quality assurance system where the ML model automatically analyzes deployment manifests and provides quality recommendations without requiring expert intervention. The system serves itself by using unsupervised clustering to identify patterns and generate recommendations autonomously, freeing developers from manual review tasks.

Inventive Principle:

Principle #25Self-service

2Reliability

If expert developers conduct code reviews to transfer knowledge, then best practices are maintained, but productivity decreases due to time consumption

Engineering Contradiction:

Improveadherence to best practicesVSAvoiddeployment development speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces expert developer code reviews with an automated ML system that encapsulates best practices within the trained model. The system analyzes deployment manifests and provides recommendations based on learned patterns from clustered data, maintaining adherence to best practices while eliminating the time constraint that limits productivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent creates a universal quality assurance system that can analyze any deployment manifest regardless of the specific domain or expertise level required. The ML model serves multiple functions: feature extraction, clustering, pattern recognition, and recommendation generation, making best practices accessible to all developers uniformly without requiring individual expert knowledge.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Difficulty of detecting and measuring

If static analysis tools are used to check deployment code, then some quality issues are detected, but the tools are insufficient for complex cloud framework issues

Engineering Contradiction:

Improvedetection of quality issuesVSAvoidcompleteness of issue detection

Core Design Contradiction:

Difficulty of detecting and measuringVSReliability

Solution Approach 1:

The patent replaces traditional static analysis tools with a machine learning-based system that can understand complex cloud framework patterns. The LLM-based approach captures semantic meaning and contextual relationships in deployment manifests, enabling detection of sophisticated issues that rule-based static tools cannot identify, thereby improving both detection capability and reliability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms the approach from fixed rule-based parameter checking to dynamic, data-driven parameter analysis. The system extracts multiple features from manifests, clusters them to identify patterns, and adapts its analysis based on learned relationships, allowing it to detect complex issues across varying cloud framework configurations that static tools with fixed parameters cannot handle.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20240362539A1Machine learning pipeline for deploying scalable distributed systems using a large language model

Publication Date: 2024.10.31 NOKIA SOLUTIONS & NETWORKS OY

AI summary

In various aspects systems and methods are provided for using a Large Language Model(s) to analyze declarative deployment code as found in deployment manifest files for deploying resources in a cloud-based environment.