Performance testing, a non-functional testing technique performed to determine the system parameters in terms of responsiveness and stability under various workload. Performance testing measures the quality attributes of the system, such as scalability, reliability and resource usage.
Performance testing can involve quantitative tests done in a lab, or in some scenarios, occur in the production environment. Performance requirements should be identified and tested. Typical parameters include processing speed, data transfer rates, network bandwidth and throughput, workload efficiency and reliability. As an example, an organization can measure the response time of a program when a user requests an action; the same can be done at scale. If the response times are so slow that it would annoy end users, then this means it should be tested to find where the bottleneck is.
Why Use Performance Testing?
An organization can use performance testing as a diagnostic aid to find information processing or communication bottlenecks within a system. Bottlenecks are a single point or component in a system’s overall function that holds back overall performance. For example, if the bandwidth is less than 1 megabit per second (Mbps), even the fastest computer will run poorly on the web. Slow data transfer rates can be hardware-specific, but they can also be caused by software-related problems, such as too many applications running at the same time, or a corrupted file in a web browser.
Performance testing can be used as a form of software testing to help determine the nature or location of a performance problem with software by highlighting where an application may fail or delay. This form of testing can also be used to make sure an organization is prepared for a predictable big event for online stores.
Performance testing can also verify that a system meets the specifications requested by the manufacturer or vendor. This process can be used to compare two or more devices or programs.
Types of Performance Testing
There are two main methods of performance testing: load testing and stress testing. However, there are other testing methods that can be used to determine performance. Some examples are as follows:
Load testing helps developers understand the behavior of a system under a specific load value. In the load testing process, an organization simulates the expected number of concurrent users and processes over a given period of time to verify expected response times and find bottlenecks. This type of test helps developers determine how many users an application or system can handle before that application or system is released. In addition, a developer can test specific functionality of an application, such as a payment basket on a web page. The team can include load testing as part of the continuous integration (CI) process, where they immediately test changes to the codebase using automation tools such as Jenkins.
Stress testing places a system at higher than expected traffic loads so developers can see how well the system works above expected capacity limits. There are two subcategories of stress tests: the wetting test and the spike test. Stress tests enable software teams to understand the scalability of their workload. Stress tests place a burden on hardware resources to determine the potential breaking point of an application based on resource usage. Resources may include CPUs, memory, and hard drives, as well as solid-state drives. System strain can also lead to slow data exchange, memory shortages, data corruption, and security issues. Stress tests can also show how long it takes for KPIs to return to normal operational levels after an incident. Stress tests can take place before or after a system is released. A kind of production environment stress test is called Chaos Engineering, and there are special tools for it. An organization can also perform a stress test approaching the expected load, using the same tools as load tests, before a predictable major event such as Black Friday in an e-commerce app.
The Soak test, also called endurance testing, simulates a continuous increase in end users over time to test the long-term sustainability of systems. During testing, the Test Engineer monitors key performance indicators such as memory usage and checks for failures such as lack of memory. Soak tests also analyze bandwidth and response times after continuous use to show whether these metrics are consistent with their state at the beginning of the test.
Another subset of stress testing, the Spike test, evaluates a system’s performance under a sudden and significant increase in simulated end users. Spike tests help determine whether a system can repeatedly handle a sudden, severe workload increase over a short period of time. Similar to stress tests, an IT team usually performs spike tests before a major event, when a system will be higher than normal traffic volumes.
The scalability test measures performance based on the software’s ability to scale performance measurement attributes up or down. For example, a scalability test can be performed depending on the number of user requests.
Capacity testing is similar to stress testing in that it tests traffic loads based on the number of users, but differs in quantity. Capacity testing looks at whether a software application or environment can handle the amount of traffic that is specifically designed to handle.