Workload Modeling System for Automated Performance Testing
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Solution Overview
Problem
Current performance testing methods lack an automated mechanism to accurately model workloads for mobile applications, leading to inefficient optimization efforts and potential system deployment failures due to inaccurate workload models and the absence of a feedback loop from production environments.
Innovation Solution
A workload modeling system that automatically generates and updates a workload model by analyzing production data and source code changes, bridging the feedback loop from production to facilitate continuous performance testing and initiate performance tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual workload modeling is performed, then workload models can be created, but the process is tedious and time-consuming
Solution Approach 1:
The system automatically collects production environment data and feeds it back to update workload models continuously. This feedback mechanism eliminates manual modeling efforts while maintaining high accuracy through real-world data from actual production systems.
Solution Approach 2:
The workload modeling system performs self-updating by automatically collecting production data, analyzing source code changes, and regenerating workload models without human intervention. The system serves itself by maintaining accurate models through automated processes.
2Reliability
If accurate workload models are created manually, then performance testing reliability improves, but the complexity of setup and maintenance increases
Solution Approach 1:
The system establishes a feedback loop that continuously monitors production environments and automatically updates workload models. This ensures testing reliability reflects current production conditions without requiring complex manual setup or maintenance of test environments.
Solution Approach 2:
The system performs preliminary actions by automatically collecting and analyzing production data before performance testing is needed. Workload models are pre-updated based on recent production behavior, ensuring reliability without last-minute complex setup.
3Measurement precision
If production data is continuously monitored, then workload model accuracy improves, but data collection and processing overhead increases
Solution Approach 1:
The system automatically collects, processes, and analyzes production data without requiring external intervention. The workload modeling system self-manages the entire data pipeline from collection to model regeneration, maintaining accuracy while optimizing processing efficiency through automation.
Solution Approach 2:
The system replaces manual data collection and model updating mechanisms with automated computational processes. This substitution of mechanical/manual operations with automated systems maintains measurement precision while dramatically improving processing efficiency.
Data Source
AI summary
System and method are provided for facilitating performance testing. The method includes mapping source code for an application under test to at least one performance test script, the at least one performance test script for executing functions of the application; determining at least one source code change from build release notes; receiving production environment data for the application; using the build release notes and the mapped source code to identify at least one functionality of the application associated with the at least one source code change; for each identified functionality, analyzing corresponding production environment data for a period of time; generating, based on the analysis, a trained workload model for the identified at least one functionality, the trained workload model providing a framework for subsequent performance testing of the application; determining one more performance tests based on the trained workload model; and automatically initiating at least one of the one or more performance tests using the trained workload model.