An Agent-Based Approach to Automating Software Performance Testing
Automating Performance Testing with Agentic AI
This work-in-progress paper explores whether and how agentic AI could support the automation of software performance testing with the long-term goal of reducing manual effort and preserving test relevance. To that end, we present an early-stage prototype that orchestrates multiple specialized agents to support performance testing tasks and investigate the following research question: To what extent can agentic AI support autonomous generation, execution, and interpretation of software performance tests with minimal human intervention?
Revolutionizing Performance Testing with AI Agents
AI-driven automation is poised to transform software performance testing, significantly reducing manual effort and accelerating development cycles. This paper highlights how agentic AI can autonomously manage complex testing workflows, from scenario generation to result interpretation, leading to faster deployments and improved software quality. Early prototypes demonstrate promising results in microservice environments, setting the stage for a new era of AI-augmented performance engineering.
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Reduction in Manual Scripting
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Faster Test Cycle Times
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Improvement in Test Coverage
Deep Analysis & Enterprise Applications
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This section provides an overview of the paper's main contributions and the problem it addresses. It sets the stage for understanding the broader implications of agentic AI in software performance testing.
75%
Reduction in Manual Scripting Potential
The proposed agentic AI framework aims to reduce manual scripting effort by autonomously generating test scenarios and interpreting results.
Agent-Based Performance Testing Workflow
Extract Context
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Generate Scenarios
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Execute Tests
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Interpret Results
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Provide Recommendations
The end-to-end workflow involves multiple specialized agents coordinating to automate the performance testing process.
A closer look at the architectural design, agent coordination, and the tools integrated within the framework.
| Feature | Agentic AI Approach | Traditional Approach |
|---|---|---|
| Scenario Generation |
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| Environment Setup |
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| Result Interpretation |
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Specialized Agents Coordinated
The prototype orchestrates nine specialized agents to handle different aspects of performance testing.
Details on the exploratory evaluation, key findings, and identified limitations that guide future research.
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Microservice Benchmarks Used
Evaluated on Hotel Reservation and Social Network from DeathStarBench, demonstrating applicability to realistic systems.
Case Study: Hotel Reservation System
Agents successfully generated and executed the full test suite, navigating the codebase and building realistic scenarios, including edge cases. Median response times of 7-8 ms and zero failures were reported, meeting targets. Challenges included initial failures in Dockerfile composition when original artifacts were missing, highlighting the need for robust environment synthesis strategies.
- ✓ Autonomous test generation and execution.
- ✓ Accurate codebase navigation and feature extraction.
- ✓ Successful handling of edge cases in scenarios.
- ✓ Median response times: 7-8 ms, 90th percentile < 12 ms.
- ✓ Zero failures, 1.5 requests/second throughput.
Case Study: Social Network System
The framework reconstructed system goals and JWT-based authentication from thrift definitions despite deliberate documentation removal. Login flows and media uploads were encoded into Locust scripts. Median response time was 1.1s, 90th percentile 1.8s, within the 5s SLA. Environment configuration remained problematic (omitted Nginx proxy) and some documentation-only functionality was missed, reinforcing the need for multi-source context.
- ✓ Reconstructed goals from code despite missing docs.
- ✓ Encoded complex flows (login, media upload) in scripts.
- ✓ Median response time: 1.1s, 90th percentile 1.8s.
- ✓ Within 5s SLA for interactive posting.
- ✓ Challenges: Nginx proxy omission, missed docs-only features.
Key Limitations Identified
Over-reliance on documentation
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Difficulty with implicit features
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Environment configuration challenges
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Limited empirical scope
The exploratory study revealed several limitations, guiding future research directions.
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Annual Savings
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Hours Reclaimed Annually
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Your AI Implementation Roadmap
Understand the phased approach to integrating agentic AI into your software development lifecycle for performance testing.
Phase 1: Proof-of-Concept Development
Initial prototype implementation of the agentic AI framework, integrating LangGraph, MCP, and PPTAM. Focus on core functionalities like context extraction, scenario generation, and basic execution.
Phase 2: Exploratory Evaluation & Refinement
Conducting case studies with DeathStarBench microservices to assess feasibility, identify limitations, and gather insights for improvement. Iterative refinement of agent behaviors and tool integrations.
Phase 3: Robustness & Validation Enhancements
Implementing systematic validation strategies for agent-generated artifacts, improving robustness to incomplete/inconsistent context, and integrating better environment synthesis. Exploring human-agent collaboration models.
Phase 4: Broader Empirical Grounding & Generalization
Expanding empirical evaluations to diverse architectural styles, conducting repeated runs, and comparative studies against manual testing to assess stability and generalizability. Researching emergent system features detection.
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