Proactive Guidance System for Shift-Left Software Issue Resolution
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Solution Overview
Problem
Traditional software development models fail to handle changing expectations and requirements effectively, leading to increased costs, longer time to market, and unexpected errors, as they are not designed to shift issue resolution to earlier levels, thereby not optimizing ticket management in application support.
Innovation Solution
An AI-enabled proactive guidance system that predicts issues and provides guidance to users to resolve problems without creating service tickets, analyzes ticket descriptions and application behavior to identify appropriate support levels, and uses historical data to offer training and collaborative learning to reduce similar issues, thereby shifting issue resolution to lower levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional software development models are used, then issue resolution follows conventional processes, but costs increase and time to market lengthens
Solution Approach 1:
The system performs preliminary classification and root cause identification of issues before full ticket resolution processes are initiated. By analyzing ticket descriptions and application behavior early in the process, the system determines support levels and potential resolutions in advance, preventing unnecessary escalation and reducing overall resolution costs while accelerating time to market.
Solution Approach 2:
The system enables self-service by providing proactive guidance to users based on classified issues and identified root causes. Users receive automated recommendations and training resources that allow them to resolve common issues independently without creating service tickets, thereby reducing ticket resolution costs and freeing up support resources for more complex problems.
2Reliability
If traditional software development models are used, then issue resolution processes remain conventional, but defect prevention opportunities are lost
Solution Approach 1:
The system performs preliminary classification of issues into different support levels based on ticket descriptions and application behavior analysis. By identifying the nature and complexity of issues before they reach higher support levels, the system enables early defect prevention at the appropriate support level, reducing the need for complex multi-level support structures while improving reliability.
Solution Approach 2:
The system replaces manual support level determination and defect analysis with automated AI-based classification and root cause identification. This substitution reduces the complexity of the support level structure by enabling automated routing and resolution recommendations, while simultaneously improving defect prevention capabilities through consistent, data-driven analysis.
3Ease of operation
If issues are resolved at higher support levels, then comprehensive resolution is achieved, but the burden on support teams increases
Solution Approach 1:
The system empowers users with self-help capabilities by providing automated classification results, root cause analysis, and targeted guidance based on their specific issues. Users can access relevant training resources and resolution steps without needing to contact support teams, thereby improving ease of operation while preserving support team capacity for handling complex, non-routine problems.
Solution Approach 2:
The system acts as an intermediary between users and support teams by performing automated classification and root cause identification. This intermediary function provides users with immediate, relevant information and guidance, improving their ability to self-resolve issues, while simultaneously reducing the burden on support teams by filtering out resolvable issues before they reach human agents.
Data Source
AI summary
Disclosed herein a system, method, and computer program product for providing proactive guidance to users in order to execute a shift left model in software delivery. As disclosed, a processor may receive an issue resolution request. The processor may further access an issue resolution request repository where the issue resolution request repository may include details related to prior issue resolution requests. The processor may subsequently classify the issue resolution request based on the details related to the prior issue resolution requests. Accordingly, the processor may identify a root cause for the issue resolution request.