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Map of Outcomes to Testing Techniques

The table below maps outcomes -- the results that you may want to achieve in your validation efforts -- to one or more techniques that can be used to accomplish that outcome.

When I am working on... I want to get this outcome... I should consider
Development Prove backward compatibility with existing callers and clients Shadow testing
Development Ensure program logic is correct for a variety of expected, mainline, edge and unexpected inputs Unit testing; Functional tests; Consumer-driven Contract Testing; Integration testing
Development Prevent regressions in logical correctness; earlier is better Unit testing; Functional tests; Consumer-driven Contract Testing; Integration testing; Rings (each of these are expanding scopes of coverage)
Development Validate interactions between components in isolation, ensuring that consumer and provider components are compatible and conform to a shared understanding documented in a contract Consumer-driven Contract Testing
Development; Integration testing Validate that multiple components function together across multiple interfaces in a call chain, incl network hops Integration testing; End-to-end (End-to-End testing) tests; Segmented end-to-end (End-to-End testing)
Development Prove disaster recoverability – recover from corruption of data DR drills
Development Find vulnerabilities in service Authentication or Authorization Scenario (security)
Development Prove implementation correctness in advance of a dependency or absent a dependency Unit testing (with mocks); Unit testing (with emulators); Consumer-driven Contract Testing
Development Ensure that the user interface is accessible Accessibility
Development Ensure that users can operate the interface UI testing (automated) (human usability observation)
Development Prevent regression in user experience UI automation; End-to-End testing
Development Detect and prevent 'noisy neighbor' phenomena Load testing
Development Detect availability drops Synthetic Transaction testing; Outside-in probes
Development Prevent regression in 'composite' scenario use cases / workflows (e.g. an e-commerce system might have many APIs that used together in a sequence perform a "shop-and-buy" scenario) End-to-End testing; Scenario
Development; Operations Prevent regressions in runtime performance metrics e.g. latency / cost / resource consumption; earlier is better Rings; Synthetic Transaction testing / Transaction; Rollback Watchdogs
Development; Optimization Compare any given metric between 2 candidate implementations or variations in functionality Flighting; A/B testing
Development; Staging Prove production system of provisioned capacity meets goals for reliability, availability, resource consumption, performance Load testing (stress); Spike; Soak; Performance testing
Development; Staging Understand key user experience performance characteristics – latency, chattiness, resiliency to network errors Load; Performance testing; Scenario (network partitioning)
Development; Staging; Operation Discover melt points (the loads at which failure or maximum tolerable resource consumption occurs) for each individual component in the stack Squeeze; Load testing (stress)
Development; Staging; Operation Discover overall system melt point (the loads at which the end-to-end system fails) and which component is the weakest link in the whole stack Squeeze; Load testing (stress)
Development; Staging; Operation Measure capacity limits for given provisioning to predict or satisfy future provisioning needs Squeeze; Load testing (stress)
Development; Staging; Operation Create / exercise failover runbook Failover drills
Development; Staging; Operation Prove disaster recoverability – loss of data center (the meteor scenario); measure MTTR DR drills
Development; Staging; Operation Understand whether observability dashboards are correct, and telemetry is complete; flowing Trace Validation; Load testing (stress); Scenario; End-to-End testing
Development; Staging; Operation Measure impact of seasonality of traffic Load testing
Development; Staging; Operation Prove Transaction and alerts correctly notify / take action Synthetic Transaction testing (negative cases); Load testing
Development; Staging; Operation; Optimizing Understand scalability curve, i.e. how the system consumes resources with load Load testing (stress); Performance testing
Operation; Optimizing Discover system behavior over long-haul time Soak
Optimizing Find cost savings opportunities Squeeze
Staging; Operation Measure impact of failover / scale-out (repartitioning, increasing provisioning) / scale-down Failover drills; Scale drills
Staging; Operation Create/Exercise runbook for increasing/reducing provisioning Scale drills
Staging; Operation Measure behavior under rapid changes in traffic Spike
Staging; Optimizing Discover cost metrics per unit load volume (what factors influence cost at what load points, e.g. cost per million concurrent users) Load (stress)
Development; Operation Discover points where a system is not resilient to unpredictable yet inevitable failures (network outage, hardware failure, VM host servicing, rack/switch failures, random acts of the Malevolent Divine, solar flares, sharks that eat undersea cable relays, cosmic radiation, power outages, renegade backhoe operators, wolves chewing on junction boxes, …) Chaos
Development Perform unit testing on Power platform custom connectors Custom Connector Testing

Sections within Testing

Technology Specific Testing

Build for Testing

Testing is a critical part of the development process. It is important to build your application with testing in mind. Here are some tips to help you build for testing:

  • Parameterize everything. Rather than hard-code any variables, consider making everything a configurable parameter with a reasonable default. This will allow you to easily change the behavior of your application during testing. Particularly during performance testing, it is common to test different values to see what impact that has on performance. If a range of defaults need to change together, consider one or more parameters which set "modes", changing the defaults of a group of parameters together.
  • Document at startup. When your application starts up, it should log all parameters. This ensures the person reviewing the logs and application behavior know exactly how the application is configured.
  • Log to console. Logging to external systems like Azure Monitor is desirable for traceability across services. This requires logs to be dispatched from the local system to the external system and that is a dependency that can fail. It is important that someone be able to console logs directly on the local system.
  • Log to external system. In addition to console logs, logging to an external system like Azure Monitor is desirable for traceability across services and durability of logs.
  • Log all activity. If the system is performing some activity (reading data from a database, calling an external service, etc.), it should log that activity. Ideally, there should be a log message saying the activity is starting and another log message saying the activity is complete. This allows someone reviewing the logs to understand what the application is doing and how long it is taking. Depending on how noisy this is, different messages can be associated with different log levels, but it is important to have the information available when it comes to debugging a deployed system.
  • Correlate distributed activities. If the system is performing some activity that is distributed across multiple systems, it is important to correlate the activity across those systems. This can be done using a Correlation ID that is passed from system to system. This allows someone reviewing the logs to understand the entire flow of activity. For more information, please see Observability in Microservices.
  • Log metadata. When logging, it is important to include metadata that is relevant to the activity. For example, a Tenant ID, Customer ID, or Order ID. This allows someone reviewing the logs to understand the context of the activity and filter to a manageable set of logs.
  • Log performance metrics. Even if you are using App Insights to capture how long dependency calls are taking, it is often useful to know long certain functions of your application took. It then becomes possible to evaluate the performance characteristics of your application as it is deployed on different compute platforms with different limitations on CPU, memory, and network bandwidth. For more information, please see Metrics.

Last update: April 3, 2024