Performance testing tests an application’s working under a workload. Load testing, stress testing, volume, and spike testing all come under performance testing categories. If performance testing is correctly done, the application will fare smoothly and successfully in the market.
However, performance testing is not a piece of cake. In spite of big budgets and good technology, businesses do not always get it right. Many ignore its importance and treat is a last-minute chore before application deployment.
We list 4 common mistakes made in performance testing:
1. Wrong Workload Model
The workload model is the in-depth plan testers write their test scripts against. The workload model has information about business processes, number of users, number of transactions per user and so on. If the workload model has inaccurate requirements, the testing process stemming from there will naturally be misdirected. Testers should work with business analysts to be realistic about figures and statistics regarding the production environment. Important questions to consider are the type of transaction that is most popular as well as the total number of transactions on regular and peak days and hours. The value of these transactions should be ascertained clearly, to know how much a business will be impacted by failing to handle a greater load. The workload model should be tuned according to changes in the application.
2. Inadequate Performance Testing Tools
Businesses often make the mistake of using inadequate tools. Performance testing is best when it is not entirely manual as it saves time and money. For this, it must be supported by automation tools. A performance testing company will analyze the project to determine tools that fit best for project purposes. Automation tools help in conducting high scale load testing. They can find relevant problems from both client-side and server-side. For mobile users, testing must be performed on real and virtual devices. The tools used for performance testing must be able to emulate the network conditions of mobile users. Other desirable tools are ones that can generate realistic data.
3. Using Too Much of Hard Cored Data
When testing, repeating the same request is not useful. In load testing, the best results are achieved when the situations are closest to reality. Using the same information over and over for testing is not how usage will practically roll out. What basically happens is that applications and technologies recognize the repeat data and automatically caches it, which gives the appearance of a faster working system than it actually is. In this case, performance testing fails to meet its objective. Testers should feed realistic data to tests to get accurate performance analysis.
4. Overloading Load Generators
A common mistake made in testing is overloading load generators. It is best to run initial tests with a smaller load i.e. 1-5 users. The number should be gradually bumped up to 100. Working with flood customer success engineers can help with monitoring load injection nodes. A node resource dashboard that shows CPU and memory metrics is a great tool for chalking out a plan for node capacity.
Businesses can avoid these mistakes by recruiting performance testing services that have the expertise to handle performance testing needs in the best of ways.
