AI Technical Debt Classification Across Code and Architecture
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Solution Overview
Problem
Organizations face challenges in managing technical debt due to rapid technological advancements, leading to outdated code, suboptimal designs, and increased maintenance costs, with conventional methods being disorganized and prone to inaccuracies.
Innovation Solution
A system utilizing artificial intelligence to assess and manage technical debt by integrating data from multiple sources, including process information, technology information, governance information, and tech ecosystem data, employing generative AI models for identification and classification, and providing actionable strategies through a dashboard and chatbot for stakeholder engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to assess technical debt, then the process is simple and requires minimal resources, but the assessment is disorganized, subjective, and prone to inaccuracies
Solution Approach 1:
The patent replaces manual, subjective assessment methods with an AI-powered automated system. The generative AI model processes code repositories, architecture diagrams, and process flows to objectively identify and classify technical debt, eliminating the inaccuracies and subjectivity inherent in conventional human-based assessment methods.
Solution Approach 2:
The patent introduces an AI intermediary system that mediates between raw technical data (code, diagrams, processes) and assessment results. This intermediary layer processes and analyzes the complex technical information, transforming it into structured insights about technical debt without requiring direct human analysis of all technical details.
2Measurement precision
If comprehensive data from multiple sources is integrated, then the assessment becomes more accurate and comprehensive, but the process becomes more resource-intensive and complex
Solution Approach 1:
The patent implements continuous data collection and processing from multiple sources including code repositories, architecture diagrams, and process flows. The AI model continuously analyzes these data streams to identify technical debt, eliminating the need for periodic manual audits and enabling ongoing, real-time assessment without interrupting development workflows.
Solution Approach 2:
The patent substitutes manual data collection and analysis with automated AI processes. The generative AI model continuously processes technical data from multiple sources, extracting relevant information about technical debt without requiring human intervention in the data gathering or initial analysis phases.
3Productivity
If automated AI assessment is implemented, then the assessment process becomes efficient and scalable, but the initial setup and integration complexity increases
Solution Approach 1:
The patent designs a multi-functional AI system that handles various assessment tasks including identifying technical debt, classifying debt types, prioritizing issues, and generating remediation strategies. This universal approach consolidates multiple assessment functions into a single system, reducing overall integration complexity compared to implementing separate specialized tools for each function.
Solution Approach 2:
The patent implements self-service capabilities where the AI system automatically collects data from technical sources, analyzes it to identify technical debt, and generates actionable insights without requiring manual configuration or intervention. The system serves itself by autonomously performing the complete assessment workflow, reducing the ongoing operational complexity despite initial setup requirements.
4Measurement precision
If detailed analysis of code and architecture is performed, then the identification of technical debt becomes more accurate, but the process becomes more time-consuming
Solution Approach 1:
The patent replaces manual code review and architecture analysis with AI-powered automated analysis. The generative AI model processes code repositories and architecture diagrams to identify technical debt patterns, achieving high accuracy in technical debt identification without requiring human analysts to manually examine each line of code or architectural detail.
Data Source
AI summary
A computing system assesses, identifies, and tracks technical debt by analyzing code repositories, application architecture diagrams, and process flows, classifying the debt into application and enterprise debt. A method involves analyzing code repositories, classifying technical debt, and utilizing an AI model. A computer-readable medium includes instructions for assessing and classifying technical debt.