Your solution is producing performance bugs in production that you did not see in staging and test environments.
You want to adjust your test and deployment procedures to avoid this problem in the future.
What should you do?
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A. B. C. D.D.
The situation described in this question is a common challenge faced by software development teams. Performance issues that surface in production but are not observed in staging or test environments can be caused by a variety of factors such as differences in data volume, network latency, and user behavior.
To avoid such issues in the future, it is essential to adjust the test and deployment procedures. Among the options given, the most effective approaches are:
B. Deploy smaller changes to production Deploying smaller changes can help isolate issues and reduce the overall risk of introducing bugs into production. Instead of deploying large batches of code changes, teams can break them down into smaller, manageable units and deploy them gradually. This approach allows for more granular monitoring and faster remediation if issues do arise.
D. Deploy changes to a small subset of users before rolling out to production. This approach, known as a canary release, involves deploying changes to a small subset of users before releasing them to the entire user base. By doing so, teams can monitor the impact of changes and detect any issues before they affect the entire user base. This can be particularly useful for detecting performance issues that may only surface under heavy load.
While A and C may be helpful in some cases, they are not as effective as B and D in addressing the specific issue described in the question. Deploying fewer changes or increasing load on test and staging environments may not necessarily help identify performance bugs, and may even increase the risk of missing issues that only occur under specific conditions.
In summary, to avoid performance bugs in production that were not detected in test and staging environments, teams should consider deploying smaller changes to production and/or deploying changes to a small subset of users before rolling out to the entire user base.