AI-Driven Application Debt Classification and Remediation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional application maintenance methods are inefficient in managing and reducing application debt, leading to increased costs, reduced agility, and limited visibility for prioritization, resulting in suboptimal performance and customer satisfaction.
Innovation Solution
An AI/ML-driven system for auto-pilot application debt management, utilizing a ticketing module, debt engine, and reporting dashboard to classify and remediate technical, functional, and operational debts, with customizable noise elimination and machine learning algorithms for data-driven decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional application maintenance methods are used, then service delivery can be maintained, but application debt accumulates and maintenance costs increase exponentially
Solution Approach 1:
The system performs preliminary classification of application debt into four categories (technical, functional, operational, knowledge) before remediation, enabling proactive identification and prioritization of debt items. This preliminary categorization allows the system to address debt issues before they accumulate and cause exponential cost increases, while maintaining current application performance levels.
Solution Approach 2:
The system implements continuous monitoring and feedback loops that track application debt metrics, incident patterns, and maintenance costs. This feedback mechanism enables dynamic adjustment of debt reduction strategies, allowing the system to optimize resource allocation and reduce maintenance costs while preserving application reliability through data-driven decision making.
2Loss of information
If more insights are generated to identify incident causes, then prioritization visibility improves, but data processing complexity increases
Solution Approach 1:
The system segments unstructured ticket data into structured categories (technical debt, functional debt, operational debt, knowledge debt) with specific attributes and metadata. This segmentation transforms complex unstructured data into organized, queryable information that improves visibility into incident causes while managing processing complexity through systematic classification frameworks.
Solution Approach 2:
The system introduces an intermediary classification layer that sits between raw ticket data and analysis tools. This intermediary structure standardizes diverse data formats and unstructured information into a consistent schema, enabling improved visibility and prioritization without directly processing the full complexity of raw data at every analytical stage.
3Productivity
If application maintenance is handled as service management, then service delivery speed improves, but application debt is not adequately addressed
Solution Approach 1:
The system merges service management processes with application debt management by integrating debt classification and remediation workflows into the existing service management framework. This combination enables simultaneous improvement of service delivery speed through efficient incident handling while addressing application quality issues through systematic debt reduction, achieving both productivity and reliability goals.
Data Source
AI summary
The present invention relates to a system and method for application debt management with zero maintenance strategy that make the applications “fit for use” and “fit for purpose”. The objective is to ensure that applications run at the lowest cost, deliver maximum performance and serve the purpose for which it was developed. The machine learning enabled debt engine of present system reads the unstructured ticket data or debts, eliminates noise, and classify the debts into one of predefined categories. This is followed by remediation of debt via either of automation or healing workbench based on predetermined priorities.