Most Common Mistakes with Load Testing
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Performance testing is a very important part of any application today. Global applications such as Facebook, Amazon, and Google are very fast and their popularity has made users get accustomed to fast and efficient applications that rarely fail. In the same way, users expect every other application that they use to have similar performance metrics, if not better.
Developers and testers rely on load testing to make sure that they eradicate any issues resulting from user loads that might affect the performance of their applications. Even though load testing might seem straightforward, most testers commit some mistakes that lead to inaccurate results. But before then, what is load testing?
What is Load Testing?
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Let us take an example where developers have come up with a robust application and conducted extensive functional and unit tests. These tests, without doubt, help testers to identify errors and eradicate them. However, the performance of the application in real life is not tested. This is where load testing comes in.
Load testing is important in measuring throughput rates, response times, and resource utilization when an application is exposed to different user loads. When load testing is done in conjunction with stress testing, developers are able to analyze any potential risks and change their code to make sure that their application meets all their requirements.
Here are some of the most common mistakes with load testing that testers and developers commit;
Use of Hardcoded Data
Some testers use hardcoded parameter values when writing their test scripts. This is one of the most common mistakes they commit. For example, a tester checking the performance of a web hosting website might come up with a test script that selects the same package and proceeds to checkout in many instances. The mistake with this approach is that a single package might have a different performance from all other packages offered on the website.
This does not mean that you have to include different parameters for all situations when writing the test scripts. However, thinking about situations that might have varying performance is important.
Ignoring the Think Time of Users
Sometimes, when testers are conducting load testing, they make requests to their applications’ servers and then measure their (servers) response. However, such kinds of tests fail when it comes to accounting for the execution time of JavaScript or even HTML rendering. This means that the results might not represent the true experience of a user.
Other testers use tests based on browsers that spin up the real browser instances while mimicking actual users by replaying test scripts. Here, developers rely on virtual user sessions to identify any underlying issues instead of using performance statistics.
However, such tests often ignore the think time of users. When conducting these tests, your test scripts are likely to move around an application faster compared to an actual user, meaning that the results obtained might not be accurate. It is, therefore, important for testers to write test scripts that take time between every step they take when testing an application.
Relying Solely on Response Time
The most common metric used when conducting load tests is response time. However, response time is not the only metric that should be used. For instance, an application that has a high rate of errors will not need a response rate to determine its performance under different user loads.
Testers need to look at other performance metrics such as requests per second, peak response time, error rate, average response time, throughput, and concurrent users. This way, they will be able to get accurate results.
Conclusion
Load tests play a very crucial role in the success of an application. They are among the principles of a better user experience. However, some testers commit some or all of the mistakes mentioned above, raising the chances of their applications’ failure.
Other testers are now using testing tools that help in conducting load tests and in effect, reduce the chances of them committing these mistakes. If an application is faulty after launch, chances are that users will seek alternative applications that meet their requirements. This might mean loss of customers and revenue, leading to business failure.
