Intelligent Software Change Routing From Build-Level Insights
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Solution Overview
Problem
The complexity of continuous software development involving multiple developers makes it difficult to effectively comprehend changes from build to build, manage artifacts, and ensure reliable and stable software deployments across a global customer base.
Innovation Solution
A data processing system utilizing a unified data platform, software change insights module, deployment insights module, and dashboard to extract, categorize, and intelligently route software deployments based on generated insights, leveraging Large Language Models (LLMs) for code summarization and deployment summarization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software deployments include many build artifacts and code variations for large-scale global applications, then the software can serve a global customer base with multiple developer contributions, but it becomes difficult to effectively comprehend and manage the contents of all software deployments for security audits and deployment routing
Solution Approach 1:
The patent segments the complex deployment management task into distinct functional modules: a unified data platform for extracting data from software release pipelines, a software change insights module for analyzing code changes, a deployment insights module for generating deployment recommendations, and a dashboard for visualization. This segmentation allows each module to handle specific aspects of deployment complexity independently, making the overall system manageable despite serving a global customer base with multiple developers.
Solution Approach 2:
The patent introduces an intermediary AI system that acts as a mediator between the complex software artifacts and human users. This AI-powered dashboard extracts, analyzes, and synthesizes information from numerous build artifacts and code variations, presenting simplified insights and recommendations to users. The intermediary translates complex technical data into actionable intelligence, reducing the cognitive burden on users while maintaining support for global-scale deployments.
2Productivity
If developers regularly merge code changes to repositories in iterative DevOps processes, then software can be continuously improved and deployed, but there is no simple way to understand the contents of all software deployments effectively for security audits and smart deployment routing
Solution Approach 1:
The patent implements feedback mechanisms where the AI system continuously analyzes software changes and provides insights back to developers and deployment systems. The software change insights module generates feedback about code modifications, and the deployment insights module provides feedback on recommended deployment strategies. This feedback loop maintains information integrity by constantly monitoring and reporting on deployment contents, enabling effective security audits and smart routing decisions without slowing down iterative DevOps processes.
Solution Approach 2:
The patent performs preliminary analysis of software changes using the software change insights module before deployments occur. By extracting and analyzing data from release pipelines in advance, the system prepares deployment insights and recommendations proactively. This preliminary action ensures that deployment content comprehension is already available when needed for security audits and routing decisions, preventing information loss without impeding rapid iterative development.
3Reliability
If a unified data platform extracts and analyzes data from software release pipelines using AI, then deployment insights can be generated to improve security and reliability, but the system complexity increases
Solution Approach 1:
The patent creates a multi-functional unified data platform that performs multiple tasks within a single system architecture. The platform simultaneously extracts data from release pipelines, generates software change insights, produces deployment insights, and provides visualization through a dashboard. This universal approach improves deployment reliability by ensuring consistent data processing and analysis across all functions while avoiding the complexity of multiple separate systems. The AI capabilities are integrated throughout, providing intelligent analysis without requiring separate specialized systems for each function.
Data Source
AI summary
A data processing system includes a processor, and a memory storing executable instructions which, when executed by the processor, causes the processor, alone or in combination with other processors, to implement: a united data platform for extracting data from a software release pipeline for specific software; a software change insights module to generate insights into changes to the specific software on a per build basis using the extracted data; a deployment insights module to generate deployment insights using the extracted data; and a dashboard to organize the generated insights and intelligently route deployment of a build to upgrade the specific software based on the generated insights to expedite deployment.