Ultimate Guide to Performance Testing
Performance testing is a kind of software testing that ensures software applications perform well under their expected workload. Why performance testing is necessary? It is because it reveals what needs to be improved before a software product is launched into the market. Specifically, it checks a software program’s speed, scalability, and stability.
What are the types of performance testing services?
There are different types of performance testing services that can be applied during the testing process:
Also known as soak testing, it evaluates how the software performs with a normal workload over an extended amount of time. Its goal is to check for system problems such as memory leaks (which occur when systems fail to release discarded memory and can impair system performance or cause it to fail).
This type of testing measures system performance (response time and system staying power) as the workload increases. (Workload could mean concurrent users or transactions.)
This form of testing determines if the software is effectively handling increasing workloads, by gradually adding to the user load or data volume while monitoring system performance. But, in some cases, the workload may stay at the same level while resources such as CPUs and memory are changed.
Also known as fatigue testing, it measures system performance outside of the parameters of normal working conditions. The software is given more users or transactions that can be handled, with a goal to measure software stability. It seeks to determine at what point the software will fail and how the software will recover from failure.
This type of stress testing evaluates software performance when workloads are significantly increased quickly and repeatedly. This means workloads are beyond normal expectations for short amounts of time.
Also known as flood testing, it determines how efficiently software performs with large, projected amounts of data.
What are the problems solved by performance testing services?
Take a look at this list of the most common performance issues that need to be recognized and addressed as early as possible:
Speed issues: These issues include slow responses and long load times.
Bottlenecking: Bottlenecks are obstructions in a system that degrade overall system performance. Bottlenecking occurs when data flow is interrupted or halted because there is not enough capacity to handle the workload.
Poor scalability: If the software cannot handle the desired number of concurrent tasks, results could be delayed, errors could increase, or other unexpected behaviors affecting disk or CPU usage, memory leaks, operating system limitations, and poor network configuration could happen.
Software configuration issues: Often, settings are not set at a sufficient level to handle the workload.
Insufficient hardware resources: Performance testing will reveal physical memory constraints or low-performing CPUs.
What is the process involved in performance testing?
Here are the fundamental steps to take when doing performance tests:
Identify the test environment.
A test environment is where software, hardware, and networks are set up to execute tests. Identifying the test environment, as well as the performance testing tools available, allows the testing team to design the test and identify the testing challenges early.
Performance test environment options include the following:
Subset of production system with fewer servers of lower specification
Subset of production system with fewer servers of the same specification
Replica of production system
Actual production system
Identify your performance testing metrics.
Why performance testing metrics are needed? Well, they help you understand the quality and effectiveness of the testing done.
Among all the metrics used in performance testing, the following are the most often used:
Response Time: Total time to send a request and get a response
Wait Time: Also known as average latency; tells developers how long it takes to receive the first byte after a request is sent
Average Load Time: Average amount of time it takes to deliver every request; a major indicator of quality from a user’s perspective
Peak Response Time: Measurement of the longest amount of time it takes to fulfill a request (When the peak response time is significantly longer than average, it may indicate an anomaly that will create problems.)
Error Rate: A percentage of requests resulting in errors compared to all requests (Errors usually occur when the load exceeds capacity.)
Concurrent Users: Also known as load size; the common measure of load — how many active users are there at any point
Requests per Second
Transactions Passed/Failed: Total number of successful/unsuccessful requests
Throughput: Measured by kilobytes per second; shows the amount of bandwidth used during the test
CPU Utilization: Amount of time the CPU needs to process requests
Memory Utilization: Amount of memory needed to process the request
Disk Time: Amount of time disk is busy executing a read or write request
Private Bytes: Number of bytes a process has allocated that cannot be shared among other processes; used to measure memory leaks and usage
Committed Memory: Amount of virtual memory used
Memory Pages per Second: Number of pages written to or read from the disk to resolve hard page faults, which occur when code that is not from the current working set is called up from elsewhere and retrieved from a disk
Page Faults per Second: Overall rate in which fault pages are processed by the processor
CPU Interrupts per Second
Maximum Active Sessions: Maximum number of sessions that can be active at once
Hit Ratios: Has to do with the number of SQL statements handled by cached data rather than expensive I/O operations; a good place to start for solving bottlenecking issues
Hits per Second: Number of hits on a web server during each second of a load test
Rollback Segment: Amount of data that can rollback at any point in time
Plan and design performance tests.
Identify key performance test scenarios that take into account user variability, test data, and target metrics. This will create one or two models.
Configure the test environment.
Prepare the test environment elements and instruments needed for monitoring resources.
Implement the test design.
Create the performance tests according to your test design.
Execute the tests.
Run the performance tests, and also monitor and capture the data generated.
Analyze then retest.
Consolidate, analyze, and report the test results. Then tweak and rerun the tests using the same parameters and then different parameters to see if there are any changes to the performance. Since improvements get smaller with each retest, stop when the CPU causes bottlenecking and then consider increasing the CPU power.
What are the best practices in performance testing?
Aside from taking the steps discussed above, consider following these best practices:
Involve developers and testers when creating a performance test environment.
Separate the performance test environment from the environment used in quality assurance testing.
Run tests in test environments that are as close to the production systems as possible.
Test as early as possible. Do not wait until the project winds down or else, you will be forced to rush testing. Remember that testing early is less costly than making major fixes at the end of the development.
Test individual units and modules — not just completed projects.
Run multiple tests to ensure consistent findings and determine metric averages.
For applications (which often involve multiple systems such as databases, servers, and services), test the individual units separately as well as together.
Calculate averages to deliver actionable performance testing metrics. Also, consider tracking outliers. These extreme measurements may reveal possible failures.
Make sure test automations are using the software in ways that real users would. This is especially significant when performance test parameters are changed.
Determine how test results will affect users. Keep in mind that real people will be using the software.
When preparing reports, consider your audience and include any system and software changes.
What are the performance testing concepts to beware of?
The following performance testing concepts can lead to mistakes or failure to follow the steps and best practices discussed in this article, and can even cost you a substantial amount of money and resources:
Experienced software developers do not need performance testing.
Lack of experience in developers’ part is not the only reason behind performance issues. Even developers who have created issue-free software in the past may still commit mistakes. A lot more variable come into play, especially when multiple concurrent users are in the system.
What works now will work then.
Do not extrapolate results, taking a small set of performance testing results and assuming they will be the same when certain elements change.
What works for others will surely work for you.
A performance test for a given set of users cannot be considered a performance test for all users. When conducting performance tests, make sure the platform and configurations work as expected.
Just one performance testing scenario will do.
Not all performance issues can be detected in just one performance testing scenario. In the middle part of the project alone, a series of performance tests that target the riskiest situations are needed, which have the greatest impact on performance. Aside from this, problems may arise even with well-planned, well-designed performance testing.
Testing individual parts is similar to testing the entire system.
Yes, it is important to isolate functions for performance testing, but the individual component test results do not equal a system-wide assessment. It may not be possible to test all the functionalities of a system, but still, a complete-as-possible performance test should be designed using the resources available. Just be sure to determine what has not been tested.
Simply adding hardware will fix performance issues.
Adding memory, processors, or servers simply increases cost without solving any problems. The more efficient software will run better and avoid potential problems that may occur even when you increase or upgrade the hardware.
What are the performance testing tools to use?
There are many tools available in the market — both free and paid. The tool you will choose will, of course, depend on several factors such as types of performance testing protocol supported, license cost, hardware requirements, platform support, etc. Here are some of the most used performance testing tools:
Apache JMeter is a 100% Java application that is designed for load testing functional behavior and measuring performance. It can be used to fabricate a heavy load on a server, group of servers, network or object to test the strength or analyze overall performance under different types of load.
http_load tests the throughput of a web server by running multiple HTTP fetches simultaneously. The tool can be configured to do HTTPS fetches also.
LoadRunner is a load testing tool that helps analyze and prevent application performance problems, as well as detect bottlenecks before deployment or upgrade.
LoadUI is an API solution that allows you to create, configure, and distribute your load tests interactively in real time. It supports all the standard protocols — AMF, HTML, HTTP(S), JDBC, POX, REST, and SOAP/WSDL.
OpenSTA, which stands for Open System Testing Architecture, is a free web load and stress testing tool that is capable of performing scripted HTTP and HTTPS heavy load tests with performance measurements from Win32 platforms.
With all the performance testing concepts written in this article, there is no doubt why performance testing is a vital part of any software project. It protects investments from product failure, as well as improves customer satisfaction, loyalty, and retention.