People & Teams

Risk-based testing is an approach to testing that helps us handle our limited resources. It’s also a valid model for years to come because it focuses testing resources where they can have the most impact—regardless of whether limitations are due to budget, tight schedules, or even the uncertainty of an unexpected situation like COVID-19. Here are some practical tips, examples, and steps you can use to adopt risk-based testing.

How can mature companies with complex systems achieve the level of test automation that modern delivery schedules and processes demand? There are four strategies that have helped many organizations finally break through the test automation barrier: Simplify automation across the technology stack, end the test maintenance nightmare, shift to API testing, and choose the right tools for your needs.

Companies that want to reduce testing costs usually try working with fewer people, or even cutting back on the amount of testing done. But with those approaches, quality usually suffers. Releasing a critical bug and suffering the subsequent pain usually costs multiple times what testing would. There are better ways to save money, and it can be done just by being smarter about our test cases and their structure.

As your QA team grows, manual testing can lose the ability to focus on likely problem areas and instead turn into an inefficient checkbox process. Using machine learning can bring back the insights of a small team of experienced testers. By defining certain scenarios, machine learning can determine the probability that a change has a serious defect, so you can evaluate risk and know where to focus your efforts.