AI-Assisted Performance Diagnostics
Performance test results tell you when an application is slow. StresStimulus helps you uncover "Why" by connecting affected transactions and requests with resource behavior and historical changes, organizing the most relevant evidence into actionable findings, and generating AI-ready summaries that can be analyzed with your preferred LLM.
Turn Performance Test Results into an Investigation
A slow transaction is a symptom. The cause may be a particular request, a server resource, a change from the baseline, or an application dependency. StresStimulus provides detailed test results and automated analysis to help narrow the investigation to the evidence most closely associated with the degradation.
Find Slow Transactions
Identify business operations whose response times degraded or failed to meet expected performance.
Locate Slow Requests
Drill from affected transactions into the underlying requests that contribute most to the slowdown.
Compare Supporting Signals
Examine server and infrastructure resource behavior and payload variation alongside user-facing performance.
Prioritize Investigation
Use automated findings and ranked contributors to focus first on the evidence most relevant to the regression.
Explain What Contributed to a Performance Regression
When Performance Monitoring detects a regression, Performance Analysis examines the current run against its baseline and brings together the most relevant evidence. The report presents key findings, supporting evidence, and ranked contributors so engineers can move from detection to investigation faster.
Regression Detected
A test or transaction shows meaningful degradation compared with its baseline.
Evidence Examined
StresStimulus evaluates performance deltas, problem areas, and resource evidence.
Contributors Ranked
Potential contributors are prioritized by their relationship to the observed regression.
Findings Presented
The Performance Analysis report summarizes what changed and the evidence supporting each finding.
Bring Multiple Diagnostic Signals into One Analysis
Performance Analysis evaluates several types of evidence instead of relying on a single metric. This helps distinguish a meaningful regression from normal baseline variation and shows where the strongest supporting signals appear.
Baseline Variability
Provides context about normal historical variation so the current change can be evaluated against expected behavior.
Request Performance Issues
Identifies requests whose performance changes contribute to the observed degradation.
Transaction Performance Issues
Highlights business transactions with significant performance changes relative to the baseline.
Resource Correlation
Looks for relationships between performance degradation and monitored server or infrastructure resources.
Rank the Contributors to the Regression
Performance Analysis does more than list changed metrics. It identifies affected tests, transactions, and requests and evaluates the magnitude and significance of their changes to help determine which items deserve attention first.
Engineers can use the ranked contributor list together with the supporting evidence and detailed test results to move directly into deeper investigation.
Performance Analysis Report
- Key findings
- Supporting evidence
- Ranked contributors
- Test, transaction, and request metric deltas
- Contribution to regression
- Significance scoring
Continue the Investigation with Detailed Test Data
Automated analysis helps identify where to look first. StresStimulus also provides the detailed request, transaction, timing, error, and monitoring data needed to investigate those findings and understand the behavior of the application under load.
- Request and transaction results
- Waterfall and timing details
- Errors and failed requests
- Server and resource monitoring
- Historical baseline context
Take Your Test Results into the LLM You Trust
StresStimulus can generate an AI Inquiry Report, a compact JSON file optimized for analysis by the LLM of your choice, such as ChatGPT, GitHub Copilot, Claude, Gemini, or Microsoft Copilot. It includes only the test result details most useful for analysis, including selected pass/fail outcomes, transaction and request summaries, performance highlights, trend points, and deterministic observations.
By excluding insignificant and sensitive data, the report gives the LLM focused context for investigating failures, bottlenecks, and performance changes without requiring access to the original project or test database.
Typical Workflow
- Run the load test
- Export the AI Inquiry JSON
- Upload it to your preferred AI assistant
- Ask questions about failures, bottlenecks, and performance changes
Explore Related Topics
Performance Monitoring & Regression Testing
Maintain test history, establish baselines, detect regressions, and identify when performance changes across runs.
Learn moreIntegrations & Observability
Connect StresStimulus with telemetry, monitoring, and observability systems that can provide additional diagnostic evidence.
Learn moreContinuous Testing & CI/CD
Automate repeatable performance tests so regressions can be detected earlier in the delivery process.
Learn moreFind Out What Is Behind Slow Performance
Evaluate StresStimulus with your own application and see how detailed results and automated Performance Analysis can help turn performance problems into actionable findings.