Performance testing

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In software engineering, performance testing is testing that is performed to determine how fast some aspect of a system performs under a particular workload. [Performance testing is a subset of Performance Engineering, an emerging computer science practice which strives to build performance into the design and architecture of a system, prior to the onset of actual coding effort.]

Performance testing can serve different purposes. It can demonstrate that the system meets performance criteria. It can compare two systems to find which performs better. Or it can measure what parts of the system or workload cause the system to perform badly. In the diagnostic case, software engineers use tools such as profilers to measure what parts of a device or software contribute most to the poor performance or to establish throughput levels (and thresholds) for maintained acceptable response time. It is critical to the cost performance of a new system, that performance test efforts begin at the inception of the development project and extend through to deployment. The later a performance defect is detected, the higher the cost of remediation. This is true in the case of functional testing, but even more so with performance testing, due to the end-to-end nature of its scope.

In performance testing, it is often crucial (and often difficult to arrange) for the test conditions to be similar to the expected actual use.

1 Technology
2 Performance specifications
3 Tasks to undertake
4 See Also
- 4.1 Newsgroups
5 Resources/References

[edit] Technology

Performance testing technology employs one or more PC's or Unix servers to act as injectors – each emulating the presence of numbers of users and each running an automated sequence of interactions (recorded as a script, or as a series of scripts to emulate different types of user interaction) with the host whose performance is being tested. Usually, a separate PC acts as a test conductor, coordinating and gathering metrics from each of the injectors and collating performance data for reporting purposes. The usual sequence is to ramp up the load – starting with a small number of virtual users and increasing the number over a period to some maximum. The test result shows how the performance varies with the load, given as number of users vs response time. Various tools, are available to perform such tests. Tools in this category usually execute a suite of tests which will emulate real users against the system. Sometimes the results can reveal oddities, e.g., that while the average response time might be acceptable, there are outliers of a few key transactions that take considerably longer to complete – something that might be caused by inefficient database queries, etc.

Performance testing can be combined with stress testing, in order to see what happens when an acceptable load is exceeded –does the system crash? How long does it take to recover if a large load is reduced? Does it fail in a way that causes collateral damage?

[edit] Performance specifications

It is critical to detail performance specifications (requirements) and document them in any performance test plan. Ideally, this is done during the requirements development phase of any system development project, prior to any design effort. See Performance Engineering for more details.

However, performance testing is frequently not performed against a specification, i.e. no one will have expressed what is the maximum acceptable response time for a given population of users, but should be. However, performance testing is frequently used as part of the process of performance profile tuning. The idea is to identify the “weakest link” – there is inevitably a part of the system which, if it is made to respond faster, will result in the overall system running faster. It is sometimes a difficult task to identify which part of the system represents this critical path, and some test tools include (or can have add-ons that provide) instrumentation that runs on the server (agents) and report transaction times, database access times, network overhead, and other server monitors, which can be analysed together with the raw performance statistics. Without such instrumentation one might have to have someone crouched over Windows Task Manager at the server to see how much CPU load the performance tests are generating (assuming a Windows system under test).

There is an apocryphal story of a company that spent a large amount optimising their software without having performed a proper analysis of the problem. They ended up rewriting the system’s ‘idle loop’, where they had found the system spent most of its time, but even having the most efficient idle loop in the world obviously didn’t improve overall performance one iota!

Performance testing almost invariably concludes that it is the software (rather than hardware) that contribute most to delays (bottlenecks) in processing data.

Performance testing can be performed across the web, and even done in different parts of the country, since it is known that the response times of the internet itself vary regionally. It can also be done in-house, although routers would then need to be configured to introduce the lag what would typically occur on public networks. Loads should be introduced to the system from realistic points. For example, if 50% of a system's user base will be accessing the system via a 56K modem connection and the other half over a T1, then the load injectors (computers that simulate real users) should either inject load over the same connections (ideal) or simulate the network latency of such connections, following the same user profile.

It is always helpful to have a statement of the likely peak numbers of users that might be expected to use the system at peak times. If there can also be a statement of what constitutes the maximum allowable 95 percentile response time, then an injector configuration could be used to test whether the proposed system met that specification.

Performance specifications should ask the following questions, at a minimum:

In detail, what is the performance test scope? What subsystems, interfaces, components, etc are in and out of scope for this test?
For the user interfaces (UI's) involved, how many concurrent users are expected for each (specify peak vs. nominal)?
What does the target system (hardware) look like (specify all server and network appliance configurations)?
What is the Application Workload Mix of each application component? (for example: 20% login, 40% search, 30% item select, 10% checkout).
What is the System Workload Mix? [Multiple workloads may be simulated in a single performance test] (for example: 30% Workload A, 20% Workload B, 50% Workload C)
What are the time requirements for any/all backend batch processes (specify peak vs. nominal)?

[edit] Tasks to undertake

Tasks to perform such a test would include:

Decide whether to use internal or external resources to perform the tests, depending on inhouse expertise (or lack thereof)
Gather or elicit performance requirements (specifications) from users and/or business analysts
Develop a high-level plan (or project charter), including requirements, resources, timelines and milestones
Develop a detailed performance test plan (including detailed scenarios and test cases, workloads, environment info, etc)
Choose test tool(s)
Specify test data needed and charter effort (often overlooked, but often the death of a valid performance test)
Develop proof-of-concept scripts for each application/component under test, using chosen test tools and strategies
Develop detailed performance test project plan, including all dependencies and associated timelines
Install and configure injectors/controller
Configure the test environment (ideally identical hardware to the production platform), router configuration, quiet network (we don’t want results upset by other users), deployment of server instrumentation, database test sets developed, etc.
Execute tests – probably repeatedly (iteratively) in order to see whether any unaccounted for factor might affect the results
Analyze the results - either pass/fail, or investigation of critical path and recommendation of corrective action