AI Exception Interpreter for Third-Party Error Resolution
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Solution Overview
Problem
Software developers face challenges in effectively handling exceptions, particularly those from third-party resources, due to the variety of causes and lack of reliable expertise, leading to wasted development resources and inefficient problem-solving.
Innovation Solution
An AI-driven exception interpreter that receives exception reports, sanitizes sensitive information, evaluates the reports using machine learning models, and generates tailored exception handling responses, which can adapt based on previous encounters and user feedback to improve handling efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a programmer uses trial-and-error approach or seeks advice from unreliable sources to address exceptions, then the programmer can eventually resolve the exception, but development resources such as time are lost or wasted
Solution Approach 1:
An AI exception interpreter is introduced as an intermediary between the programmer and the exception resolution process. The interpreter receives exception reports, analyzes them using machine learning models, and provides recommended resolutions, eliminating the need for programmers to rely on trial-and-error or unreliable external sources.
Solution Approach 2:
The manual exception handling process (programmer analysis and resolution) is replaced with an automated AI-based system. The machine learning evaluator automatically analyzes exception reports and generates resolution recommendations, substituting human effort with an automated intelligent system.
2Adaptability or versatility
If the exception is from a third party resource, then the variety of causes and best responses is so widely varied, but the needed expertise may be beyond even skilled programmers
Solution Approach 1:
The AI exception interpreter is designed to handle multiple types of exceptions from various third-party resources through a single unified system. The machine learning evaluator can analyze different exception reports and provide appropriate resolutions across diverse scenarios, making the system universally applicable.
Solution Approach 2:
The system uses machine learning models that can adapt to different exception types by changing their analysis parameters and evaluation criteria dynamically. This allows the same system to handle varied third-party exceptions without requiring specialized expertise for each specific case.
3Reliability
If the programmer lacks expertise to optimally address an exception, then the exception can be resolved, but development resources are wasted
Solution Approach 1:
The system enables self-service exception handling where the AI interpreter autonomously analyzes exception reports and generates resolution recommendations without requiring programmer expertise. The catch passage automatically transmits exception reports to the interpreter and implements the recommended resolutions.
Solution Approach 2:
The system incorporates feedback mechanisms where exception handling outcomes are fed back into the machine learning model, continuously improving the quality of resolution recommendations. This feedback loop enhances both the reliability of exception resolution and the efficiency of the development process.
Data Source
AI summary
Example solutions for providing an artificial intelligence (AI) exception interpreter are disclosed. An exception interpreter receives an exception report from a software application, evaluates it (using AI), and generates a first exception handling response. This provides the benefit of learning over a large amount of exception handling data, which offers super performance over trial-and-error tactics by programmers who are either less experienced or unfamiliar with exceptions from third party resources. Some examples sanitize exception reports of sensitive information. Some examples track an exception event history, so that when the same exception is seen multiple times (meaning a prior response was not effective), a different exception handling response may be provided.