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Quantify Captured Performance Requirements
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If you are lucky, most of the performance requirements that you captured are already quantified and testable. If you are a little less lucky, the requirements you captured are not quantified at all, in which case you can follow the process described above for quantifying performance goals. If you are unlucky, the performance requirements that you collected are partly quantified and non-testable. The challenge is that if a requirement is extracted from a contract or existing marketing document, it likely cannot be changed. When you are faced with a requirement such as three-second average response time, or 2,500 concurrent users, you have to figure out what those requirements mean and what additional information you need in order to make them testable. There is no absolute formula for this. The basic idea is to interpret the requirements precisely written in common language, supplement them with the most common or expected state for the application, and then get your extended, testable requirement approved by the stakeholder(s). The stakeholders will then be held responsible if someone were to challenge legal compliance with the requirements after the product goes live. To illustrate, consider the following examples: Requirement: Direct quote from a legal contract: The Website shall exhibit an average response time of not greater than three (3) seconds. Extended quantification: This requirement is particularly challenging. The literal, and therefore most likely legal, interpretation is that Over the life of the Website, the arithmetic mean of all response times, at any point in time, will not exceed 3 seconds. While that is hard enough to determine, response time has not been defined either. Response time could mean end-user-perceived response time, server response time, or something else entirely. The following breaks this down systematically: Without any information to the contrary, it is probably safe to assume that the only reasonable way to test the three-second average response time is either with all pages being accessed equally often or under the most likely workload distribution. Again, without any information to the contrary, you are left to determine the load conditions for the test. In this case, your best bet is probably to average across multiple volumes. For instance, you could get 30 percent of your data from low-load tests, 50 percent from expected-load tests, and 20 percent from high-load tests, and then report a weighted average assuming that distribution of load is a reasonable approximation of the anticipated production load profile. Alternatively, you could make a case for testing this requirement exclusively under expected load conditions. Requirement: Direct quote from sales brochure: This application will support up to 2,500 concurrent users. Extended quantification: The challenge here is similar because concurrent user is not technically accurate for Web applications and therefore can mean several different things.
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Since it is unlikely that you will have the opportunity to determine the intention of the person who chose the term concurrent, you have to use your best judgment based on the application. Generally, the safest interpretation is overlapping, active sessions where an active session is one user s activity between the time they access the application until the time they complete their task without stopping to do something else whether or not the application technically tracks sessions. Using this interpretation, if a user typically has session duration of 15 minutes, statistically, it would take a total of about 5,000 users over a 30-minute period with a realistic ramp-up/ramp-down model to simulate 2,500 overlapping active sessions. Also, in this example you have no information about the expected activity of those users. As in the previous example, it is probably safe to assume that the only reasonable way to test this requirement are either with all pages being accessed equally often or under the most likely workload distribution although in this case, under the mostly likely workload distribution is more likely to be the original author s intent.
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