Dynamic Error Detection via Rank Scored Logging
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Solution Overview
Problem
In programmatic environments with complex asynchronous dependencies, identifying the root cause of errors is inefficient due to excessive logging, which affects reliability, usability, and performance, and current methods require manual specification of logging points and observation of method execution.
Innovation Solution
A system that uses defined execution conditions and condition triggers to automatically collect log data only when errors occur, calculating rank scores to identify the most probable root cause methods and dynamically instrumenting them for detailed error logging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed logging is implemented to locate root cause of problems, then measurement precision of errors is improved, but use of energy and runtime cost increase
Solution Approach 1:
The system applies different logging qualities to different parts of the system. Critical methods that are likely to cause errors receive detailed logging instrumentation, while non-critical methods use minimal or no logging. This localized approach to logging quality maintains error detection precision for important components while reducing overall runtime cost and energy consumption.
Solution Approach 2:
The system implements partial logging by selectively instrumenting only the subset of methods that are most likely to cause errors, rather than logging all methods. The error detection system identifies and focuses logging resources on critical paths and high-risk methods, achieving sufficient measurement precision with reduced logging overhead.
2Reliability
If a larger set of logs is included to troubleshoot problems, then reliability of error detection is improved, but productivity of software system decreases
Solution Approach 1:
The system enhances error detection reliability by concentrating logging efforts on critical methods and execution paths where errors are most likely to occur. Rather than uniformly logging all methods, the system identifies high-risk areas and applies detailed logging only there, maintaining detection reliability while preserving overall system productivity.
Solution Approach 2:
The error detection system automatically identifies and instruments critical methods without requiring manual specification of all logging points. The system self-adapts to focus logging resources on the most problematic areas, improving reliability while minimizing the performance overhead that would result from comprehensive logging.
3Ease of operation
If dynamic instrumentation is used to collect log data, then ease of operation for error collection is improved, but device complexity increases
Solution Approach 1:
The system automatically performs error detection and logging without requiring developers to manually specify trace-points or interactively enable/disable logging. The error detection system self-identifies critical methods, automatically instruments them with appropriate logging, and manages the entire error collection process autonomously, improving ease of operation while the automation manages the inherent complexity.
Solution Approach 2:
The system performs preliminary analysis to identify critical methods and execution paths before error occurrence. By pre-instrumenting only the identified critical methods with logging capability, the system simplifies the operational process of error collection while the preliminary identification and selective instrumentation manage the system complexity.
4Loss of time
If automatic logging is implemented only when errors occur, then loss of time for logging is reduced, but measurement precision may be insufficient
Solution Approach 1:
The system applies different logging strategies to different methods based on their criticality. For automatically identified critical methods, detailed logging is implemented to maintain measurement precision. For non-critical methods, minimal or no logging is performed, reducing overall logging overhead time while preserving precision where it matters most.
Solution Approach 2:
The system implements partial logging by focusing detailed logging only on the subset of critical methods that are most likely to cause errors. This selective approach reduces the total logging overhead time compared to comprehensive logging, while maintaining sufficient measurement precision for error detection in the critical areas.
Data Source
AI summary
System and methods are provided for optimal error detection in programmatic environments through the utilization of at least one user-defined condition. Illustratively, the conditions can include one or more triggers initiating the collection of log data for methods associated with the provided at least one condition. Operatively, the disclosed systems and methods observe the run-time of the programmatic environment and initiate the collection of log data based on the occurrence of a condition trigger. A rank score can also be calculated to rank the methods associated with the defined condition to isolate those methods that have higher probability of causing the defined condition. Dynamic instrumentation of the methods associated with the user defined conditions during run time are used to calculate the rank score, which is used for ranking the methods.