What are the current trends and innovations in system testing methodologies?

1 Model-based testing

Model-based testing (MBT) is a system testing methodology that uses models to represent the system under test, its requirements, and its test cases. Models can be graphical, textual, or mathematical, and can capture different aspects of the system, such as its structure, behavior, data, or interfaces. MBT can automate the generation, execution, and evaluation of test cases, based on the models and predefined test criteria. MBT can also facilitate the communication and collaboration among stakeholders, and enable early detection and correction of defects.

2 Shift-left testing

Shift-left testing is a system testing methodology that advocates for testing activities to be performed as early as possible in the system engineering life cycle, rather than waiting for the system to be fully developed and integrated. Shift-left testing aims to reduce the cost and risk of system testing, by identifying and resolving issues in the initial stages of design, development, and verification. Shift-left testing can also improve the feedback loop and the alignment between the system requirements and the system implementation.

3 Continuous testing

Continuous testing is a system testing methodology that integrates testing into the continuous delivery pipeline, where the system is built, tested, and deployed in short and frequent cycles. Continuous testing enables fast and frequent feedback on the system quality, performance, and functionality, and supports agile and DevOps practices. Continuous testing requires automation, orchestration, and monitoring of the testing processes and tools, as well as collaboration and coordination among the system engineering team and the stakeholders.

4 Risk-based testing

Risk-based testing (RBT) is a system testing methodology that prioritizes and allocates testing resources based on the level of risk associated with each system feature, function, or component. Risk can be assessed based on the probability and impact of failure, the complexity and volatility of the system, the user expectations and satisfaction, and the regulatory and compliance requirements. RBT can help optimize the system testing process, by focusing on the most critical and relevant aspects of the system, and reducing the waste and redundancy of testing.

5 AI-driven testing

AI-driven testing is a system testing methodology that leverages artificial intelligence (AI) techniques and technologies to enhance and augment the system testing capabilities and outcomes. AI-driven testing can use machine learning, natural language processing, computer vision, and other AI methods to automate, optimize, and improve various aspects of system testing, such as test case generation, test data creation, test execution, test analysis, test reporting, and test maintenance. AI-driven testing can also enable adaptive and intelligent testing, where the system testing process can learn from the system behavior and the test results, and adjust accordingly.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?