Synthetic Baseline

Detailed explanation

A synthetic baseline in software testing is a crucial element for performance testing and monitoring, particularly when a real-world baseline is unavailable or impractical to obtain. It involves creating an artificial benchmark that represents the expected performance of a system under a defined set of conditions. This baseline serves as a reference point against which future performance measurements can be compared to identify regressions, anomalies, or improvements.

The primary reason for using a synthetic baseline is often the lack of a suitable real-world baseline. This can occur in several scenarios:

• New Systems: When a system is brand new and has not yet been deployed to a production environment, there is no historical data to use as a baseline.
• Significant Changes: After a major architectural change, upgrade, or migration, the old baseline may no longer be relevant.
• Unstable Environments: If the production environment is highly variable or subject to unpredictable workloads, it can be difficult to establish a reliable real-world baseline.
• Specific Load Conditions: When you need to understand performance under specific, controlled load conditions that are difficult to replicate in a live environment.

Creating a Synthetic Baseline:

The process of creating a synthetic baseline typically involves the following steps:

1. Define the Scope: Clearly define the system components and transactions that will be included in the baseline. This should align with the key performance indicators (KPIs) that are important to the business.
2. Model the Workload: Develop a realistic workload model that simulates the expected user behavior and data patterns. This may involve analyzing existing usage patterns or making educated assumptions based on requirements.
3. Configure the Test Environment: Set up a test environment that closely mirrors the production environment in terms of hardware, software, and network configuration.
4. Execute the Tests: Run the performance tests with the simulated workload and collect performance metrics such as response time, throughput, resource utilization (CPU, memory, disk I/O), and error rates.
5. Analyze the Results: Analyze the collected data to establish the baseline values for each KPI. This may involve calculating averages, percentiles, and standard deviations.
6. Document the Baseline: Document the baseline values, the test environment configuration, and the workload model in detail. This documentation is essential for future comparisons and troubleshooting.

Practical Implementation and Best Practices:

• Realistic Workload Modeling: The accuracy of the synthetic baseline depends heavily on the realism of the workload model. Invest time in understanding user behavior and data patterns to create a model that accurately reflects real-world usage. Tools like JMeter, Gatling, and LoadRunner can be used to simulate user traffic. For example, using JMeter, you could define different user scenarios with varying request rates and data payloads to mimic real user behavior.

  <HTTPSamplerProxy guiclass="HTTPDefaultsGui" testclass="HTTPSamplerProxy" testname="HTTP Request" enabled="true">
    <elementProp name="HTTPsampler.Arguments" elementType="Arguments" guiclass="HTTPArgumentsPanel" testclass="Arguments" testname="User Parameters" enabled="true">
      <collectionProp name="Arguments.arguments"/>
    </elementProp>
    <stringProp name="HTTPSampler.domain">example.com</stringProp>
    <stringProp name="HTTPSampler.port">80</stringProp>
    <stringProp name="HTTPSampler.path">/api/resource</stringProp>
    <stringProp name="HTTPSampler.method">GET</stringProp>
    <boolProp name="HTTPSampler.follow_redirects">true</boolProp>
    <boolProp name="HTTPSampler.auto_redirects">false</boolProp>
    <boolProp name="HTTPSampler.use_keepalive">true</boolProp>
    <boolProp name="HTTPSampler.DO_MULTIPART_POST">false</boolProp>
    <boolProp name="HTTPSampler.BROWSER_COMPATIBLE_MULTIPART">false</boolProp>
    <stringProp name="HTTPSampler.embedded_url_re"></stringProp>
    <stringProp name="HTTPSampler.connect_timeout"></stringProp>
    <stringProp name="HTTPSampler.response_timeout"></stringProp>
  </HTTPSamplerProxy>

• Representative Test Environment: Ensure that the test environment is as close as possible to the production environment. This includes hardware specifications, software versions, network configuration, and data volume. Containerization technologies like Docker can help create consistent and reproducible test environments.

• Controlled Conditions: Minimize external factors that could influence the test results. This includes isolating the test environment from other systems, controlling network traffic, and ensuring that the test environment is not subject to resource contention.

• Automated Testing: Automate the process of running the performance tests and collecting the performance metrics. This will make it easier to repeat the tests and compare the results against the baseline. Tools like Jenkins or GitLab CI/CD can be used to automate the testing process.

• Regular Updates: The synthetic baseline should be updated regularly to reflect changes in the system, the workload, or the environment. This will ensure that the baseline remains relevant and accurate.

• Statistical Analysis: Use statistical methods to analyze the performance data and establish the baseline values. This will help to identify outliers and ensure that the baseline is statistically significant.

• Monitoring and Alerting: Integrate the synthetic baseline into the monitoring system to detect performance regressions or anomalies. Set up alerts to notify the team when performance deviates significantly from the baseline. Tools like Prometheus and Grafana can be used for monitoring and visualization.

Common Tools:

• JMeter: A popular open-source load testing tool that can be used to simulate user traffic and collect performance metrics.
• Gatling: Another open-source load testing tool that is known for its high performance and scalability.
• LoadRunner: A commercial load testing tool that offers a wide range of features and capabilities.
• Prometheus: An open-source monitoring system that can be used to collect and store performance metrics.
• Grafana: An open-source data visualization tool that can be used to create dashboards and alerts based on performance metrics.
• Docker: A containerization platform that can be used to create consistent and reproducible test environments.

By carefully creating and maintaining a synthetic baseline, software development and QA teams can proactively identify and address performance issues, ensuring that the system meets its performance requirements and delivers a positive user experience. The key is to ensure the synthetic environment closely mirrors the production environment and that the simulated load accurately reflects real-world usage patterns.

Further reading

• Performance Testing Guidance
• Load Testing vs. Performance Testing
• Gatling Documentation