Transport Request Error Prediction and Targeted Training
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Solution Overview
Problem
Incorrect transport requests can lead to system downtime, data loss, and damage to applications or processes due to errors such as syntax errors, integration errors, and test case errors, often caused by inexperienced personnel.
Innovation Solution
A system that predicts or measures metrics associated with transport requests to identify errors and proposes relevant training courses for the responsible personnel, using a non-transitory computer-readable medium with instructions to configure a processor for preparing and evaluating transport requests, simulating updates, and assigning risk scores or quality ratings based on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software updates are deployed through transport requests, then system functionality is improved and bugs are fixed, but system errors may occur leading to downtime and data loss
Solution Approach 1:
The system performs preliminary evaluation of transport requests by simulating the update process and assessing metric values before actual deployment. This advance checking identifies potential errors in syntax, activation, integration, and test cases, preventing harmful updates from reaching the production system and causing downtime or data loss.
Solution Approach 2:
A transport manager system acts as an intermediary between software development and production deployment. This mediator evaluates transport requests using multiple metrics, simulates updates, and controls the deployment process, thereby filtering out erroneous updates before they can harm the production system while still allowing valid updates to proceed.
2Measurement precision
If transport requests are thoroughly evaluated before deployment, then error detection is improved, but processing time and complexity increase
Solution Approach 1:
The evaluation system divides the transport request assessment into multiple independent metric evaluations, including syntax checks, activation checks, integration checks, and test case validations. Each metric is evaluated separately and compared against threshold values, allowing comprehensive error detection through modular, manageable components rather than a monolithic complex system.
Solution Approach 2:
The system creates a simulation copy of the update process to evaluate transport requests without affecting the actual production system. By copying the deployment environment and running metric evaluations on this replica, the system achieves thorough error detection while isolating the complexity of the evaluation process from the production system.
3Measurement precision
If transport requests are thoroughly evaluated before deployment, then error detection is improved, but processing time increases
Solution Approach 1:
The system performs preliminary metric evaluations and simulations before actual deployment to identify errors early in the process. By conducting syntax checks, activation checks, integration checks, and test case validations in advance, the system detects errors before they reach production, preventing costly downtime and data loss that would occur much later if errors were not caught.
Solution Approach 2:
The system uses threshold-based metric comparisons to quickly determine whether transport requests are acceptable. By evaluating metric values against predefined thresholds and making rapid pass/fail decisions, the system processes transport requests efficiently without excessive delays, balancing thorough error detection with acceptable processing time.
Data Source
AI summary
The subject matter disclosed herein provides methods and apparatus, including computer program products, for proposing training based on the prediction or measurement of different metrics associated with a transport request. In one aspect there is provided a method that may include preparing a transport request including one or more objects configured to provide an update of an application. The transport request may be associated with one or more metrics, each metric having a metric value. The method may also include determining whether the transport request includes at least one error by at least evaluating the metric value for each of the one or more metrics and comparing the metric value for each of the metrics with a threshold value; and proposing, based on the determining, at least one training course when the transport request includes the at least one error. Related systems, apparatus, methods, and/or articles are also described.