This is the fifth #BestPractices blog post of a series, by Kevin Parker.
Excellent application performance and reliability is crucial in today’s software-dependent business environment. That’s why load testing — simulating realistic user loads to assess application performance — is a cornerstone of quality assurance. However, load testing can be resource-intensive, both in terms of time and budget. Given that, this blog post explores six techniques that minimize load testing costs without compromising application quality.
Best Practice Techniques
1. Address Calendar Challenges
One primary load testing hurdle is finding a suitable slot in the team’s calendar. Development timelines are increasingly tight, so it’s not uncommon for load testing to be scheduled after the release date. Such a delay can obviously lead to expensive fixes and, worse, poor user experience and brand perception. To overcome this challenge, it’s essential to prioritize load testing early in the development process. One approach to better allocate resources and time, and to prevent last-minute crises, is to perform load testing on a parallel track rather than as an afterthought.
2. Embrace a Holistic Approach
Load testing should be integrated into your application quality assessment from all angles. Don’t treat it as a standalone activity. Rather, view it as part of a 360-degree approach to ensuring application quality. This approach not only enhances application reliability but also streamlines the testing process by identifying potential issues across different dimensions.
3. Do Regular and Frequent Load Testing
Load testing shouldn’t be a one-time affair. Make it a regular practice in order to catch performance regressions early and to optimize resource utilization. Frequent load testing helps you detect potential bottlenecks, memory leaks, and scalability issues in their infancy, reducing the likelihood of expensive troubleshooting later on.
4. Leverage the AIQ Software Testing Platform
Utilizing a unified quality platform like AIQ can significantly cut down load testing costs. For instance, AIQ offers seamless capabilities between functional and performance testing by allowing you to use existing scripts without any modifications. This accelerates the setup process while also reducing the learning curve for your team.
5. Utilize Autocorrelation
Autocorrelation is a technique that streamlines your load testing efforts and is robustly supported by AIQ. It enables you to strip out repetitive API calls from the script, enabling you to run a significant portion of virtual users (VUs) as API tests. This optimizes test execution and also provides a clearer focus on user experience testing, which is often more resource-intensive.
6. Perform Scalability Testing for Performance Insights
While load testing provides a snapshot of how an application behaves under varying loads, scalability testing takes it a step further. Scalability testing reveals the application’s performance curve and highlights the point at which it starts to plateau. This insight is invaluable to understand the application’s capacity and identify potential performance ceilings. By focusing on scalability, you can make informed decisions about resource allocation and architectural improvements, leading to efficient scaling and reduced costs.
Conclusion
Load testing needn’t drain your resources. By implementing these six best practices, you can ensure effective load testing that aligns with your development schedule and budget constraints. Start by prioritizing load testing early in the development cycle, integrating it holistically into your quality assessment, and making it a recurring practice. Leverage an AI-driven testing platform like AIQ to simplify the setup process and streamline test execution. Employ techniques like autocorrelation to optimize testing efforts, focusing on both API tests and user experience. Finally, embrace scalability testing to gain deeper insights into application performance and make informed scaling decisions.
Remember, cost-effective load testing isn’t about cutting corners, but rather about optimizing your approach to ensure the highest quality applications while maximizing resource utilization.
This is the fifth #BestPractices blog post of a series, by Kevin Parker.
For a complete resource on all things Generative AI, read our blog “What is Generative AI in Software Testing.”
