Software Process Issue Detection with Threshold-Based Alerts
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Solution Overview
Problem
Existing enterprise computing systems face challenges in efficiently detecting and proactively addressing technical issues that delay or disrupt operational processes, leading to increased user dissatisfaction and support ticket overload due to delayed issue detection.
Innovation Solution
A system that monitors key performance indicators and triggers notifications when predefined thresholds are exceeded, allowing for early detection and resolution of technical issues, thereby reducing the need for individual user support tickets and facilitating quicker issue resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system waits for user support tickets to detect technical issues, then users can report problems independently, but the support team becomes overwhelmed and detection is delayed
Solution Approach 1:
The system performs preliminary monitoring of process execution status and performance metrics before users experience significant delays. By continuously tracking process state and comparing against expected behavior patterns, the system detects anomalies early and notifies users proactively, preventing the accumulation of support tickets.
Solution Approach 2:
The system establishes a feedback loop that continuously monitors process execution and provides real-time information about system health to both users and support teams. This feedback mechanism enables early detection of technical issues through automated monitoring of process status, performance metrics, and error patterns.
2Reliability
If the system monitors all processes continuously, then technical issues are detected early, but system complexity and resource consumption increase
Solution Approach 1:
The monitoring system is segmented into modular components that can be independently deployed and configured. Different monitoring agents are assigned to specific processes or process types, allowing the system to scale monitoring coverage by adding or removing individual monitoring modules rather than overhauling the entire system.
Solution Approach 2:
The monitoring system uses universal process state tracking mechanisms that can monitor multiple different process types with a single framework. By identifying common patterns in process execution and using generic monitoring logic that adapts to specific process requirements, the system reduces complexity while maintaining broad monitoring capability.
3Reliability
If the system sends notifications for every anomaly, then all technical issues are communicated to users, but false notifications increase user fatigue
Solution Approach 1:
The system applies partial notification by selectively alerting users only when monitoring data crosses predefined thresholds or exhibits patterns consistent with genuine technical issues. Rather than notifying for every minor anomaly, the system filters notifications based on severity, frequency, and impact criteria, reducing false alarms while maintaining detection sensitivity.
Solution Approach 2:
The system performs preliminary analysis of monitoring data using historical patterns and baseline metrics before triggering notifications. By comparing current process state against established norms and predicting potential issues before they fully manifest, the system reduces false notifications while maintaining high detection accuracy for genuine problems.
Data Source
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AI summary
Systems and methods include monitoring of one or more software applications to determine a value of a first metric associated with instances of a first process, the first process including steps executed by the one or more software applications, determination that the value of the first metric has exceeded a threshold associated with the first process in a first number of ongoing instances of the first process, determination that the first number is greater than a first count limit associated with the first process, and, in response to the determination that the first number is greater than the first count limit, sending of an error message to a user associated with each of the ongoing instances of the first process.