AI DevOps Platform Correlating Data to Automate Traceability
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Solution Overview
Problem
Software development and information technology operations (DevOps) tools operate in silos, lacking standard metadata, collaboration mechanisms, and data sharing, leading to inefficient resource usage and manual traceability challenges, which wastes computing and networking resources.
Innovation Solution
An artificial intelligence platform that correlates data from various DevOps tools, trains models like software impact analyzers and defect lifecycle optimizers, to identify impacted files, developers, and automate defect handling, thereby improving productivity and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If DevOps tools operate independently in silos without integration, then each tool can maintain its own functionality and simplicity, but resource usage becomes inefficient and traceability becomes manual and time-consuming
Solution Approach 1:
The patent merges multiple DevOps tools into an integrated platform that shares common metadata and infrastructure. The system combines source code management, build automation, testing, and deployment tools into a unified ecosystem where data flows automatically between components, eliminating the need for separate siloed operations and reducing redundant resource consumption.
Solution Approach 2:
The integrated DevOps platform provides universal functionality that serves multiple purposes across different development stages. The same metadata infrastructure supports code tracking, build monitoring, test management, and deployment validation, allowing a single system to perform diverse functions that previously required separate specialized tools.
2Loss of information
If DevOps tools lack standard metadata and collaboration mechanisms, then each tool maintains operational independence, but data sharing becomes difficult and manual traceability is required
Solution Approach 1:
The patent introduces a centralized metadata service that acts as an intermediary between different DevOps tools. This mediator component standardizes data exchange formats and provides a common language for tools to communicate, enabling automatic data sharing without requiring complex point-to-point integration logic between each tool pair.
Solution Approach 2:
The system implements standardized metadata parameters that transform how tools exchange information. By defining consistent data structures, schemas, and identification protocols across the platform, the system changes the parameters of data interaction from proprietary and incompatible formats to standardized, machine-readable metadata that enables automatic traceability and collaboration.
3Loss of time
If manual traceability methods are used to track software development processes, then system complexity remains low, but time consumption increases and productivity decreases
Solution Approach 1:
The integrated DevOps platform implements self-service automation where the system automatically generates and maintains traceability information without human intervention. As code is committed, built, tested, and deployed, the platform automatically creates traceability links between these stages using the standardized metadata, eliminating the need for manual tracking while maintaining complete visibility into the software lifecycle.
Solution Approach 2:
The system implements continuous feedback loops that automatically update traceability information as development progresses. Sensors and monitoring components throughout the DevOps pipeline feed data back to the central metadata service, which maintains real-time traces of code origins, build histories, test results, and deployment status, providing automatic and accurate traceability throughout the software lifecycle.
Data Source
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AI summary
A device may receive data associated with a software development platform, and may correlate the data to generate correlated data. The device may train a first model, with the correlated data, to generate a software impact analyzer model, and may train a second model, with the correlated data, to generate a software development behavior model. The device may receive data identifying a new software requirement associated with the software development platform, and may process the data identifying the new software requirement, with the software impact analyzer model, to identify a file or a module impacted by the new software requirement. The device may process data identifying the file or the module, with the software development behavior model, to identify a developer to handle the new software requirement, and may perform one or more actions based on the data identifying the file or the module and data identifying the developer.