Introduction to Software performance testing

In software quality assurance, performance testing is in general a testing practice performed to determine how a system performs in terms of responsiveness and stability under a particular workload. It can also serve to investigate, measure, validate or verify other quality attributes of the system, such as scalability, reliability and resource usage.

Performance testing, a subset of performance engineering, is a computer science practice which strives to build performance standards into the implementation, design and architecture of a system.

Load testing:

Load testing is the simplest form of performance testing. A load test is usually conducted to understand the behavior of the system under a specific expected load. This load can be the expected concurrent number of users on the application performing a specific number of transactions within the set duration. This test will give out the response times of all the important business critical transactions. The database, application server, etc. are also monitored during the test, this will assist in identifying bottlenecks in the application software and the hardware that the software is installed on.

Stress testing:

Stress testing is normally used to understand the upper limits of capacity within the system. This kind of test is done to determine the system’s robustness in terms of extreme load and helps application administrators to determine if the system will perform sufficiently if the current load goes well above the expected maximum.

Soak testing:

Soak testing, also known as endurance testing, is usually done to determine if the system can sustain the continuous expected load. During soak tests, memory utilization is monitored to detect potential leaks. Also important, but often overlooked is performance degradation, i.e. to ensure that the throughput and/or response times after some long period of sustained activity are as good as or better than at the beginning of the test. It essentially involves applying a significant load to a system for an extended, significant period of time. The goal is to discover how the system behaves under sustained use.

Spike testing:

Spike testing is done by suddenly increasing or decreasing the load generated by a very large number of users, and observing the behavior of the system. The goal is to determine whether performance will suffer, the system will fail, or it will be able to handle dramatic changes in load.

Breakpoint testing:

Breakpoint testing is similar to stress testing. An incremental load is applied over time while the system is monitored for predetermined failure conditions. Breakpoint testing is sometimes referred to as Capacity Testing because it can be said to determine the maximum capacity below which the system will perform to its required specifications or Service Level Agreements. The results of breakpoint analysis applied to a fixed environment can be used to determine the optimal scaling strategy in terms of required hardware or conditions that should trigger scaling-out events in a cloud environment.

Configuration testing:

Rather than testing for performance from a load perspective, tests are created to determine the effects of configuration changes to the system’s components on the system’s performance and behavior. A common example would be experimenting with different methods of load-balancing.

Isolation testing:

Isolation testing is not unique to performance testing but involves repeating a test execution that resulted in a system problem. Such testing can often isolate and confirm the fault domain.

Internet testing:

This is a relatively new form of performance testing when global applications such as Facebook, Google and Wikipedia, are performance tested from load generators that are placed on the actual target continent whether physical machines or cloud VMs. These tests usually requires an immense amount of preparation and monitoring to be executed successfully.

Setting performance goals:

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.
  • It can measure which parts of the system or workload cause the system to perform badly.


Performance testing technology employs one or more PCs 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: to start with a few virtual users and increase the number over time to a predetermined 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 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, pictures, 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 its failure cause collateral damage?

Analytical Performance Modeling is a method to model the behavior of a system in a spreadsheet. The model is fed with measurements of transaction resource demands (CPU, disk I/O, LAN, WAN), weighted by the transaction-mix (business transactions per hour). The weighted transaction resource demands are added up to obtain the hourly resource demands and divided by the hourly resource capacity to obtain the resource loads. Using the response time formula (R=S/(1-U), R=response time, S=service time, U=load), response times can be calculated and calibrated with the results of the performance tests. Analytical performance modeling allows evaluation of design options and system sizing based on actual or anticipated business use. It is therefore much faster and cheaper than performance testing, though it requires thorough understanding of the hardware platforms.

The above is a brief about Software performance testing. Watch this space for more updates on the latest trends in Technology.

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