Practice : Load & Performance Testing

Purpose and Strategic Importance

Load and Performance Testing validate how a system behaves under expected, peak, and stress conditions. These practices are essential to ensure reliability, responsiveness, and stability of applications - especially those with high concurrency, transaction volume, or uptime expectations.

By uncovering bottlenecks before users experience them, teams can tune performance, avoid outages, and build systems that scale confidently under real-world demand.

Description of the Practice

  • Load testing simulates normal to peak user traffic to evaluate system capacity.
  • Stress testing pushes beyond normal limits to identify failure points and degradation behaviours.
  • Performance metrics include response time, throughput, error rate, and resource utilisation.
  • Tools include JMeter, Gatling, k6, Locust, Artillery, and cloud-based solutions like Azure Load Testing and AWS Performance Insights.
  • Performance tests are automated, version-controlled, and integrated into CI/CD workflows.

How to Practise It (Playbook)

1. Getting Started

  • Identify critical endpoints, workflows, or services to test under load.
  • Define performance goals (e.g. response time under 1s at 500 RPS).
  • Use load testing tools to simulate realistic traffic and monitor system metrics.
  • Run tests in isolated environments with observability in place to capture performance data.

2. Scaling and Maturing

  • Automate performance tests as part of release pipelines or nightly builds.
  • Test against scaled environments with production-like configurations.
  • Introduce scenarios like spike testing, endurance testing, and chaos under load.
  • Analyse performance data to inform optimisation and scaling strategies.
  • Establish SLOs/SLAs based on measured results and business expectations.

3. Team Behaviours to Encourage

  • Collaborate with product, ops, and platform teams on realistic test conditions.
  • Make performance tuning a shared responsibility - not just post-release firefighting.
  • Use performance results in planning and backlog refinement.
  • Discuss bottlenecks and improvements in retros and architecture reviews.

4. Watch Out For…

  • Performance tests that are too synthetic or unrealistic.
  • Ignoring test results that reveal poor scalability or instability.
  • Tests run too infrequently to catch regressions early.
  • Lack of baseline metrics - making trend analysis impossible.

5. Signals of Success

  • Teams have confidence in how systems respond under load.
  • Performance regressions are detected early and resolved quickly.
  • SLAs and customer expectations are backed by real test evidence.
  • Scaling decisions are informed by data, not assumptions.
  • Performance testing is a continuous, integrated part of delivery.