# PRD: Automated Performance Testing for Dynamic Thresholds

This PRD focuses on implementing automated performance tests to validate AIVA's dynamic threshold adjustment capabilities under simulated load.

### GPM-085: Implement Basic Load Simulation Framework
**Goal:** To establish a foundational framework for generating realistic load patterns on the AIVA system.
**Acceptance Criteria:**
- A script can be executed to simulate a specified number of concurrent users/requests.
- The script can be configured to vary the load intensity over time (e.g., ramp-up, steady state, ramp-down).
- The simulation framework logs the number of active users/requests at regular intervals.
**Files:** `performance_tests/load_simulation.py`, `performance_tests/config.yaml`
**Spawns:** Extend the load simulation framework to support different types of simulated user behavior.

### GPM-086: Measure Key Performance Indicators (KPIs)
**Goal:** To automatically collect relevant performance metrics during load simulations.
**Acceptance Criteria:**
- The system automatically measures and logs response times for key AIVA functionalities (e.g., data ingestion, anomaly detection, alert generation).
- The system records CPU utilization, memory usage, and network I/O during the simulations.
- The KPIs are stored in a structured format (e.g., CSV, JSON) for analysis.
**Files:** `performance_tests/metrics_collector.py`, `performance_tests/kpi_definitions.yaml`
**Spawns:** Visualize the collected performance metrics in a dashboard for real-time monitoring.

### GPM-087: Define Baseline Load Test Scenario
**Goal:** To establish a standard load test scenario for evaluating AIVA's performance.
**Acceptance Criteria:**
- A load test scenario is defined, specifying the number of simulated users, request patterns, and duration.
- The scenario is documented and reproducible.
- The scenario targets a representative workload for AIVA.
**Files:** `performance_tests/scenarios/baseline.yaml`, `documentation/performance_test_scenarios.md`
**Spawns:** Create additional load test scenarios to cover different workload profiles.

### GPM-088: Run Baseline Load Test and Record Results
**Goal:** To execute the baseline load test and capture performance data for subsequent analysis.
**Acceptance Criteria:**
- The baseline load test can be executed automatically using the established framework.
- The test results, including KPIs and logs, are stored for analysis.
- The duration of the test is configurable.
**Files:** `performance_tests/run_test.py`, `results/baseline_test_results.csv`
**Spawns:** Analyze the baseline test results to identify performance bottlenecks and areas for optimization.

### GPM-089: Validate Dynamic Threshold Adjustment under Load
**Goal:** To verify that AIVA's dynamic thresholds adapt correctly under the baseline load scenario.
**Acceptance Criteria:**
- The performance tests track the dynamic thresholds during the load simulation.
- The dynamic thresholds adjust appropriately based on the observed system behavior.
- The system generates alerts only when performance deviates significantly from the dynamically adjusted thresholds.
**Files:** `performance_tests/threshold_validation.py`, `results/threshold_data.csv`
**Spawns:** Implement automated alerts when the dynamic thresholds fail to adjust as expected.

### GPM-090: Automate Performance Test Reporting
**Goal:** To generate automated reports summarizing the results of performance tests.
**Acceptance Criteria:**
- An automated script generates a report summarizing the KPIs, threshold adjustments, and alert activity during the performance test.
- The report includes visualizations of the data.
- The report is generated in a human-readable format (e.g., PDF, HTML).
**Files:** `performance_tests/report_generator.py`, `templates/performance_report.html`
**Spawns:** Integrate the performance test reporting with a continuous integration/continuous deployment (CI/CD) pipeline.
