Testing In Production

Testing in production refers to the practice of validating software in the environment where it will ultimately operate and be accessible by end users. This method differs significantly from testing in isolated development or staging environments. Here's why:

  • Real-world conditions: Unlike controlled test environments, production offers the complexity of real user interactions and live data.

  • Immediate feedback: By exposing new features to live traffic, you get instant insights into their functionality, performance, and stability.

  • Enhanced reliability: Testing in production helps ensure that the software performs well under typical customer usage scenarios.

This approach leverages the actual user environment to validate software updates, providing a clear picture of how new features will perform in the hands of users. It's a critical step in delivering a seamless user experience and maintaining high software quality.

Examples of testing in production

A/B testing with live user traffic

Consider how companies like Netflix engage in A/B testing by exposing two feature versions to real users within their production environment. This approach garners immediate insights into user preferences and behaviors, guiding enhancements based on actual usage data.

Feature flags for incremental rollouts

Feature flags are tools that developers employ to selectively activate new functionalities for specific user segments. By toggling these features on or off, the team can test impact and stability incrementally, thus safeguarding the broader user base from potential disruptions.

Real-time monitoring and adjustment

In dynamic sectors like e-commerce, real-time monitoring of user interactions with new features is crucial. This enables immediate troubleshooting or adjustments by the development team, maintaining service quality and enhancing user satisfaction without notable interruptions.

Benefits of testing in production

Realistic feedback is a key advantage when you test in the actual user environment. It gives you a clear picture of how features perform under real-world conditions. This direct approach helps in refining the user experience based on genuine user interactions. For further understanding on production environments, you can explore How Statsig Works.

Quick issue detection and resolution becomes feasible with immediate exposure to live traffic. This method allows for faster identification and fixing of problems, which minimizes any adverse effects on the user experience. It's all about being proactive rather than reactive. Tools like Statsig's feature management can be essential in managing this process efficiently.

Enhanced confidence in releases results from the direct testing of software in the production environment. You can be more certain that the software will function as intended upon release. This assurance builds trust in the deployment process and improves overall release management. Detailed insights into this can be gained through Statsig's experimentation features.

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Why the best build with us

OpenAI OpenAI
Brex Brex
Notion Notion
SoundCloud SoundCloud
Ancestry Ancestry
At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
OpenAI
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
Brex
Karandeep Anand
President
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
Notion
Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
SoundCloud
Don Browning
SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
Ancestry
Partha Sarathi
Director of Engineering
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