Automated Exception Resolution via Contextual History Storage
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Solution Overview
Problem
Software development is hindered by complex and time-consuming exception resolution processes, where exception messages from compilers or runtime environments are often unhelpful, leading to prolonged debugging times and increased costs due to the lack of recorded contextual information for recurring exceptions.
Innovation Solution
A method is implemented where a computing device monitors software development sessions, detects exceptions, and stores contextual information for future reference, allowing automatic recognition of recurring exceptions and presentation of previous encounter details to aid in rapid resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If exception messages are provided by compilers or runtime environments, then exception identification is enabled, but the exception messages are unhelpful and do not provide sufficient contextual information for resolution
Solution Approach 1:
The system performs preliminary action by monitoring and storing contextual information about exceptions as they occur during software development sessions. This creates a historical record of exceptions and their contexts before they need to be resolved again, enabling faster future resolution by retrieving previously stored contextual information.
Solution Approach 2:
The system implements feedback by detecting when an exception occurs, retrieving previously stored contextual information about that exception, and presenting it to the developer. This feedback loop provides relevant historical context that helps developers resolve exceptions more efficiently.
2Reliability
If developers manually track and document exception encounters, then contextual information is preserved, but the process becomes time-consuming and increases development costs
Solution Approach 1:
The system enables self-service by automatically monitoring software development sessions, detecting exceptions, storing contextual information, and retrieving relevant data without requiring manual intervention from developers. This automated self-service approach maintains reliable exception records while preserving development productivity.
Solution Approach 2:
The system replaces the mechanical manual process of tracking and documenting exceptions with an automated computational system. The processor automatically monitors sessions, detects exceptions, stores contextual information in storage devices, and retrieves data, substituting manual mechanical documentation efforts with automated electronic processes.
3Loss of information
If comprehensive contextual information is collected and stored for every exception, then exception resolution accuracy improves, but system complexity and storage requirements increase
Solution Approach 1:
The system applies extraction by selectively capturing and storing only the relevant contextual information associated with exceptions during software development sessions. Rather than recording all system activities, it extracts and stores specifically the exception-related context that would be useful for future resolution, reducing unnecessary data storage while maintaining resolution accuracy.
Data Source
AI summary
Automated exception resolution during a software development session based on previous exception encounters is disclosed. A first software development session of a user is monitored. A first exception during the first software development session is detected. In response to detecting the first exception, first contextual information is obtained from the computing device. The first contextual information and first exception information that identifies the first exception is stored in a storage device.