Deterministic Software Root-Cause Reporting with Causal Event IDs
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Solution Overview
Problem
Existing software systems struggle to accurately and efficiently connect negative outcomes to their root cause conditions, leading to significant troubleshooting challenges due to the complexity of data flows, varying cause-and-effect relationships, and the intractable number of possible failure modes, resulting in prolonged downtime and resource wastage.
Innovation Solution
A causal event database is implemented with unique causal IDs to track and link parent and child events, using Boolean operations to propagate these IDs, allowing users to quickly identify root causes through graphical interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional error tracking methods are used in software systems, then the system can operate with simpler data structures and less overhead, but the ability to accurately identify root causes of errors deteriorates
Solution Approach 1:
The error tracking system is segmented into distinct components: causal IDs that uniquely identify cause-and-effect relationships, parent-child event linking structures, and hierarchical organization of events. This segmentation allows precise root cause identification by breaking down complex error chains into manageable, traceable units without overwhelming system complexity
Solution Approach 2:
Causal IDs serve as intermediary elements that bridge causes and effects in software systems. These identifiers act as mediators that carry diagnostic information through the system, enabling accurate root cause tracking without requiring complex direct analysis of cause-and-effect relationships at every level
2Loss of information
If comprehensive error tracking is implemented to capture all possible failure modes, then the completeness of diagnostic information improves, but the time and resources required for troubleshooting increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing causal ID links between parent and child events as errors occur. This preliminary structuring of diagnostic information allows troubleshooting to begin from an organized state, reducing the time needed to analyze failure modes while maintaining comprehensive diagnostic coverage
Solution Approach 2:
The error tracking system adds a temporal and hierarchical dimension to error data by organizing events in parent-child relationships with causal IDs. This dimensional organization transforms comprehensive but potentially overwhelming error information into a structured hierarchy that can be efficiently navigated, reducing troubleshooting time while preserving diagnostic completeness
3Reliability
If detailed causal tracking is implemented across all software components, then the reliability of error diagnosis improves, but the computational overhead and system complexity increase
Solution Approach 1:
The system extracts only the essential causal identification information (causal IDs and parent-child links) from complex error data, separating this critical diagnostic information from other error details. This extraction maintains high diagnosis reliability by focusing on key causal relationships while reducing computational overhead by not tracking every possible error attribute
Data Source
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AI summary
Negative outcomes experienced by a user in a live software system can be automatically, deterministically, and contemporaneously traced back to the root conditions that caused those outcomes, by generating causal event entries in a database for those root conditions as they occur, assigning unique causal IDs to those causal events, and propagating causal IDs alongside the software system state changes that are known to produce negative outcomes and which are effected by those root conditions. By selectively passing causal IDs based on the input and output values of the operation, subsequent causal events and negative outcomes can be linked accurately to causal IDs of parent events, making it simpler to trace negative outcomes for the user back to their root cause events in a software system.