A software product is tested for various technical parameters including its performance under various levels of workload and stress. A vital element in the performance test design is that of providing for the future. Design, code, and the execution environment affect software performance. Whenever a software application is built, it must be understood that the deployment is going to be made, not in a static but in very dynamic environments. As such, little bit of forecasting for the future is core to the success of such virtual products.
Models to predict performance
Predictive technology has lent a futuristic dimension to the performance engineering process. The predictive model is deployed during the early stages of software product development and is a preventive approach when compared to the late-cycle measurement-based approach. Performance is predicted from scenarios, architecture, and product or process design. The effect of potential changes and additions are quantified and extrapolated.
Business Intelligence and Predictive Analytics
Big data analytics is one of the best ways to monetize your data. The science behind timely decision making is validated and well-researched data. Every project is unique and the timing of the introduction of performance testing can vary in the future. This is where predictive analytics pitches in with its iterative approach that can remove subjective biases in performance testing. Sensible planning, meticulous data collection, multivariate analysis, timely deployment and accurate reporting are the future-oriented techniques followed in performance tests that can lead to a successful forecast of the future.
AI-powered automated optimization in Performance Engineering
From the era of manual optimization of tests, Artificial Intelligence has emerged as a potential predictor of the future. Even without writing code, the concepts powered by machine learning can create and run tests. Thus, codeless test automation gears your performance design for the future. The DevOps team of the next generation bats for CI (Continuous Integration) based model, using which performance testing can be consistently adapted to different real-life scenarios, like high traffic and skeleton availability of internet.
From baseline testing to fully scaled tests
Baseline tests, as the name suggests are very basic tests that are conducted by the PE team. Forecasting begins with statistical concepts such as time-series analysis and regression analysis, that help you form, test, and validate the hypothesis and take your performance test designs to the next level and scale.
As control mechanisms to test out the product performance before it goes live, performance tests need to be future-looking. Performance models from various scenarios must be future-adjusted so that the final products stand the test of not only capacity, volume, load, stress and spike, but that of time. Only when consistent results are obtained over time, the product can be unleashed in real scenarios. Accommodation for the future is thus an indispensable element when configuring the test environment.