Testing performance

When big changes are made to an application, some assurance is sought regarding the performance impact of the new release. Whilst performance testing is still not universally adopted, it is the de facto standard to 'prove' that a given application can perform adequately on release. There are many tools on the market, HP LoadRunner being the market leader, but they all essentially work in the same way - by simulating an artificial load onto a test environment and observing the performance of the result. This is such a standard approach that it's not often thought about, and is often considered a checkbox to release rather than to consider the intrinsic value of the exercise. However, some industry leaders adopt a risk-based strategy to testing which takes advantage of performance modelling technology to supplement the testing cycles. Performance Modelling has the advantages of:

• Providing greater flexibility than testing alone. With performance testing, you can prove the performance of a given application configuration on a specific hardware platform. With performance modelling, you can predict the performance of that application under many different configurations and hardware platforms. Expect to drive efficiencies and optimization projects with a greater success rate.
• Provides quicker value: often saving weeks from the project plans. How? Since Performance Testing depends on a properly configured evironment, various teams must get involved to prepare the ground for the test to be run. With Modelling, you can get there quicker; with a few mouse clicks you can evaluate several alternative configurations. Expect to save weeks from the cycle for every release.
• Reduces cost of testing. For full value, performance testing often seeks to test against full-spec test labs. However, these can be hugely expensive to buy and maintain. Performance Modelling has none of these costs. Expect to save 90% of the cost of testing.
• Accuracies. With reliable performance modelling techniques, expect to be hitting 90% accuracy levels on your capacity and performance predictions. Not as good as testing, but bang-for-buck a winner every time.

Those companies who adopt this risk-based strategy to testing, don't eliminate the need for performance testing - rather they drive cost savings and efficiency savings that traditional approaches can't provide.