Analyzing Performance Test Data
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Carles Roch-Cunill, Test Lead for System Performance at McKesson Medical Imaging Group, shared his expertise in Analyzing Performance Test Data.
![Analyzing Performance test data (or how to convert your numbers to information) Carles Roch-Cunill Test Lead for System Performance McKesson Medical Imaging Group [email_address]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Analyzing Performance Test Data
- 2. Analyzing Performance test data (or how to convert your numbers to information) Carles Roch-Cunill Test Lead for System Performance McKesson Medical Imaging Group [email_address]
- 10. Errors, forget them at your own risk In the graph besides. If your error bar is ± 1, we can say the trend is to a larger value. However, if the error bar is ± 3, then we can not say anything about the trend of this data
- 13. About the meaning of data But, what exactly the requirement means? Strictly it means:
Notas do Editor
- Remind that usually performance requirements fall into the categories of non-functional requirements. More often than not are done at the integration level, where multiple steps are required. This talk will only performance parameters of your system. We will not focus in complementary values like OS performance indicators
- Here we are under the assumption that we have some Performance problems, or at least the possibility of performance degradation However, event if the test case does not fail, you may be curious to know where the improvement (if any) in performance occur. The “But there may be others…” it by itself another hypothesis!
- We can introduce here the concept of percentile In a normal distribution, “event A should not take more than X second 50% of the time” is equivalent to the “Event A average should be X”
- There are sophisticated mathematical formulas to evaluate the statistical significance of a batch of data, but in everyday life there is not enough time to gather the appropriate amount of data and the time to analyze it and apply these equations.
- Based on the same criteria, we can argue that results for test 2 also are not statistically equivalent.
- Now that we have some statistical concepts we can analyze the data.
- This is non-uniform distribution
- Usually, when you are measuring data, you have some model you are explicitly or implicitly using. The most common one, both explicit and implicit is the normal distribution.
- Models are useful to idealize reality and to make sense of your data. But your model can be wrong or incomplete. For example you may implicitly assume you are working with a real time system, but software applications running in Windows or Unix are not real time systems.
- “The analysis of the performance data…” You can observer trends. For example in the “Time to query” graph, if we increase the number of queries that join a large number of tables, then our performance will degrade. With this in mind we can optimize the database or change the queries. “The analyzed results…” this is because computer systems are not linear. For example we can predict the behaviour of a system versus the network utilization, but it will be a point in which the network will became unusable.