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Scalability risks concern not only the number of users an application can support, but also the volume of data the application can contain and process, as well as the ability to identify when an application is approaching capacity. Common scalability risks that can be addressed via performance testing include: Can the application provide consistent and acceptable response times for the entire user base Can the application store all of the data that will be collected over the life of the application Are there warning signs to indicate that the application is approaching peak capacity
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Will the application still be secure under heavy usage Will functionality be compromised under heavy usage Can the application withstand unanticipated peak loads
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The following strategies are valuable in mitigating scalability-related risks: Compare measured speeds under various loads. (Keep in mind that the end user does not know or care how many other people are using the application at the same time that he/she is.) Design load tests that replicate actual workload at both normal and anticipated peak times. Conduct performance testing with data types, distributions, and volumes similar to those used in business operations during actual production (e.g., number of products, orders in pending status, size of user base). You can allow data to accumulate in databases and file servers, or additionally create the data volume, before load test execution. Use performance test results to help stakeholders make informed architecture and business decisions. Work with more meaningful performance tests that map to the real-world requirements. When you find a scalability limit, incrementally reduce the load and retest to help you identify a metric that can serve as a reliable indicator that the application is approaching that limit in enough time for you to apply countermeasures. Validate the functional accuracy of the application under various loads by checking database entries created or validating content returned in response to particular user requests. Conduct performance tests beyond expected peak loads and observe behavior by having representative users and stakeholders access the application manually during and after the performance test.
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Stability is a blanket term that encompasses such areas as reliability, uptime, and recoverability. Although stability risks are commonly addressed with high-load, endurance, and stress tests, stability issues are sometimes detected during the most basic performance tests. Some common stability risks addressed by means of performance testing include: Can the application run for long periods of time without data corruption, slowdown, or servers needing to be rebooted If the application does go down unexpectedly, what happens to partially completed transactions When the application comes back online after scheduled or unscheduled downtime, will users still be able to see/do everything they expect When the application comes back online after unscheduled downtime, does it resume at the correct point In particular, does it not attempt to resume cancelled transactions
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Can combinations of errors or repeated functional errors cause a system crash Are there any transactions that cause system-wide side effects Can one leg of the load-balanced environment be taken down and still provide uninterrupted service to users Can the system be patched or updated without taking it down
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The following strategies are valuable in mitigating stability-related risks: Make time for realistic endurance tests. Conduct stress testing with the key scenarios. Work with key performance indicators (network, disk, processor, memory) and business indicators such as number of orders lost, user login failures, and so on. Conduct stress testing with data feeds that replicate similar business operations as in an actual production environment (e.g., number of products, orders in pending status, size of user base). You can allow data to accumulate in databases and file servers, or additionally create the data volume, before stress test execution. This will allow you to replicate critical errors such as database or application deadlocks and other stress failure patterns. Take a server offline during a test and observe functional, performance, and dataintegrity behaviors of the remaining systems. Execute identical tests immediately before and after a system reboot. Compare the results. You can use an identical approach for recycling services or processes. Include error or exception cases in your performance test scenarios (for example, users trying to log on with improper credentials). Apply a patch to the system during a performance test. Force a backup and/or virus definition update during a performance test.
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Summary
Almost all application- and business-related risks can be addressed through performance testing, including user satisfaction and the application s ability to achieve business goals. Generally, the risks that performance testing addresses are categorized in terms of speed, scalability, and stability. Speed is typically an end-user concern, scalability is a business concern, and stability is a technical or maintenance concern. Identifying project-related risks and the associated mitigation strategies where performance testing can be employed is almost universally viewed as a valuable and time-saving practice.
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