Asher Hartwell
An extension of the earlier List that deals with various techs and trends in Software and application Testing
One of the most common mistakes in testing strategy is treating UI and API tests as alternatives. They're not. They complement each other. UI tests give you the user perspective. API testing gives you backend truth. Together, they make your testing stronger, your bugs easier to diagnose, and your releases safer.
As agile projects grow, so does the regression test suite. Efficiently scaling Agile Regression Testing involves refactoring tests, removing obsolete cases, and leveraging parallel execution to keep testing fast and effective across multiple releases.
Every software release introduces potential risks, but non-regression testing minimizes them. It allows QA teams to validate that core functions are untouched by new updates, giving developers, stakeholders, and customers confidence in the stability and reliability of each version.
It’s a myth that codeless test automation can only handle basic test cases. Modern platforms now support complex workflows, condition handling, data-driven testing, and even API and cross-browser scenarios—bridging the gap between simplicity and sophistication.
Modern DevOps and CI/CD pipelines rely on fast iterations—but speed must not come at the cost of security. A secure test infrastructure ensures that test automation, integrations, and artifacts are all protected throughout the pipeline, reducing exposure to internal and external threats.
Automation works best when applied strategically, yet teams often over-automate or under-automate. The automation challenge lies in identifying which tests deliver real value, where automation can reduce risk, and when manual testing is still the better choice.
Test infrastructure is nothing but the environment, testing tools, and useful resources that one uses to test software. A test infrastructure can be on-premise, cloud-based, or hybrid, each having its unique benefits. You need to choose the right infrastructure suited for your testing needs.
By ignoring AI testing, organizations miss out on smarter test execution, risk-based prioritization, and predictive analytics. This not only hampers release quality but also sets QA efforts behind competitors who are already embracing intelligent automation.
Model-based testing reduces maintenance overhead by abstracting tests from code. When application logic changes, only the model needs updates—not each individual test. This centralization makes model-based testing a highly maintainable and cost-effective solution.
Manual regression testing is time-consuming and error-prone. Automated regression testing tools drastically reduce the time required for each test cycle, enabling faster feedback and quicker identification of issues.
Choosing between sanity testing vs regression testing depends on your testing goals. If you need to validate a quick fix or a newly added feature, sanity testing is your go-to. If you're rolling out a major update or want to ensure previous functionality still works, regression testing is critical.
Whether working solo or in large dev teams, confidence in code quality comes from robust unit testing. Unit Testing Tools like RSpec, Spock, and JUnit allow teams to write expressive, maintainable test cases that align with agile workflows and reduce the cost of defects.
Simulating cloud testing features like real-device access, geographic coverage, and scalable test automation in on-premise infrastructure demands significant investment. Cloud testing's on premise challenges include a lack of automated provisioning, slower updates, and hardware limitations, which introduce delays and hinder innovation in testing workflows.
Learn what POS testing is and why it’s essential for your retail success. Explore testing methods, key challenges, and ensure your systems run smoothly.
