Software Testing Basics for 2026: What's Changed and Why it Matters

If you learned your software testing basics the old-school way, with manual exploratory sessions, constant test updates due to brittle selectors, and a lengthy testing run right at the end of the development process, you’ll have noticed that things are changing. 

This is largely due to AI. Thanks to advances in AI tech, automated software testing in 2026 isn’t just faster or smarter – it can be more autonomous, more security-focused, and more adaptable to complex, dependency-heavy systems that previously would have been beyond the reach of test automation. 

This shift in technology means that our collective idea of ‘software testing basics’ needs to evolve. In 2026, these trends are accelerating and maturing, and the second half of the 2020s is when AI-led features will really come into themselves. Here’s what this ‘new normal’ means for your team’s routine software testing activities. 

You Should Embrace Agentic AI as a Major Efficiency Tool

Today’s agentic AI testing tools use autonomous ‘agents’ that plan, execute, monitor, and adapt tests – and the more they test, the smarter they get. 

These aren’t just script generators. Think of them as more like a junior QA team member that’s always on, exploring your app, triaging failures, and even proposing fixes or patches for tests and environments. They need a lot less supervision (though you should, of course, always review suggested changes) and a lot less coffee. 

Why It Matters

2026’s agentic AIs take the technology from an on-the-horizon tool to software testing basic used by mainstream engineering teams around the world. 

Automation can now be continuous. Traditional automation executes fixed test cases; agentic systems explore user journeys, discover untested flows, and create new cases on the fly, offering much broader, more realistic coverage. Agentic AI also accelerates feedback loops – agents can run targeted tests when code changes, triage results, and flag true regressions against environment flakes, reducing noise in CI/CD.

How to Incorporate Agentic AI

• Start with good governance: cost/benefit analysis, cost control, audit trails for agent decisions, and precautions against unsafe actions (such as changes in production) are vital for any successful agentic AI implementation.
• Start small: use agents on low-risk flows first (such as a sandboxed UI), then measure ROI, and refine your scope before committing to a wider rollout
• Shop around: platform vendors are rapidly shipping agent functionality in a rush to attract new business. Do your due diligence here – demos, reviews, recommendations from your professional network, and customer testimonials help you find a quality product, not one that’s been rushed to market prematurely. 
• Keep one eye on regulatory developments – at all times: In 2026, major provisions from the EU AI Act come into force. Globally, more regulation will likely follow. Anticipate an extra compliance burden as AI matures as a technology, keep up with developments in the area, and don’t get caught out. 

(Good) Self-Healing is Now a Software Testing Basic. Find a Tool You Don’t Need to Babysit

Self-healing tests aren’t exactly new on the scene. The functionality has been around a while – but let’s be honest, traditional auto-healing tools were always a little temperamental. Which is a polite way of saying ‘not that good’. 

The difference in 2026? Rigid heuristics that heal the wrong element, auto-wait features that don’t work with dynamic pages, and poor change logging are out. Smart, intent-based locators, dynamic waits, and transparency are in. AI makes self healing genuinely good – self-healing features are now a software testing basic you can trust day-to-day. 

Why It Matters

Think of all the time your engineers will save – both on manual test maintenance and on babysitting old-school auto-healing features that are supposed to save you time, not introduce further uncertainty into test results.  Here’s how: 

• Dynamic locator strategies: Test runners identify when a UI element’s selector changes and either find a robust alternative (based on DOM structure, text, and accessibility attributes) or update the test to use a semantic selector
• Flake detection and automatic retries: Platforms can distinguish timing/environment flakes from real failures and apply safe retry logic or rerun rules
• Automated patch suggestions: Instead of making a human edit, systems can propose changes (or create pull requests) to keep test suites functional while preserving auditability

Read more about Momentic’s approach to self-healing tests

How to Incorporate Self-Healing Tests

Look for tools that offer:

• Flexible intent-based natural language selectors: These automatically update when the DOM changes, in contrast to hardcoded CSS selectors or XPaths which increase brittleness
• AI-enhanced element detection: This offers more accurate location of replacement elements based on visual cues, accessibility data, and position in the DOM – selectors adapt as your UI changes
• Smart waiting and stabilization: Smart waiting features that take into account dynamic page components (for example, nav bars and ad popups) know when your page has stabilized. This reduces timing issues and flakiness 
• Failure recovery (auto-patching steps): Look for tools that propose step changes to continue the test run, whilst flagging for human review. This ensures traceability and transparency for robust audit trails

It’s good practice to combine self-healing tools with observable metrics for healing events, flake rates, and changes suggested/made. This allows you to measure test effectiveness and spot regressions. You should always have an eye on initial code quality – don’t assume that self-healing features will do it all for you. 

You may also want to set policy limits for automated fixes. For example, you could allow automated fixes for non-critical UI updates, but require human approval for deeper logic changes.

Security Threats: Continuous Testing is Now Essential

Another ‘year ahead in software testing’ article. Another reminder that ‘cyber threats are continuously evolving’. Predictable? Absolutely, but also very important. 

Into 2026 and beyond, there are a few developing trends you’ll have to contend with to minimize your app’s vulnerability to cyber threats: 

• Attackers using more automation and AI to find and weaponize vulnerabilities
• Software stacks becoming more complex due to third-party packages, agentic services, and dynamic infrastructure
• Attacks targeting embedded AI models and agents with prompt injection and data-leakage 
• Business logic abuse via targeting legitimate features, such as rate limits, promotions, and refunds
• API and misconfiguration risks caused by services splitting into APIs –  misconfigurations and overly-permissive interfaces are common attack targets

Why It Matters

Yep –the bad guys are using AI too. This means they will find more vulnerabilities faster, and adapt more quickly to any pre-emptive security measures you put in place. And, given increasing reliance on third-party services in software stacks, there are potentially more vulnerabilities to exploit. 

So, it’s all doom and gloom? Maybe – but only if you don’t take the right precautions. Most cyber attacks target the easiest prey, so your number one takeaway here should be that security testing needs to move away from periodic scans to continuous, context-aware security validation.

Doing this will allow you to keep pace with the evolution of cyber threats, and make your app a significantly less tempting target for cyber criminals. 

How to Implement Continuous Security Testing

• Shift security testing left: Shift-left isn’t just for quick regression tests – it’s good practice to embed security checks into unit, integration, and system tests whilst testing as you go. Use threat modeling as part of your acceptance criteria as part of an ‘always on’ approach
• Expand your test coverage: There’s always a chance that vulnerabilities are hidden in nonessential flows that aren’t a priority for extensive testing. Use a low-code testing tool to increase coverage (with next to no extra time commitment) to minimize the risk of these slipping through
• Continuous security pipelines: Run dependency scans, static/dynamic analysis, and configuration checks in CI, and automate SBOM checks and vulnerability gating
• Test AI components explicitly: You should include adversarial prompts, data-handling tests, and privacy checks for agents that access real data – and also validate model outputs against safety policies and log tool calls for audit purposes 
• Incorporate more security measures: Automated tests should include hardened API fuzzing and permission checks as part of smoke and release gates
• Use agentic testing to surface logic abuse by simulating adaptive, multi-step adversarial flows

Momentic: The New Standard for Software Testing Basics

The easier your software testing tool is to use, the easier it is to shift left, address security concerns on a continuous basis, and expand your test coverage. 

Momentic is an agentic AI testing tool designed for engineers, by engineers with zero tolerance for sluggish tests or poor software design. That’s why we’ve designed Momentic to be plug-and-play from day one, with: 

• Natural language test creation: write what you want to test in plain English, and you’re good to go – no code needed
• Smart self-healing tools with intent-based selectors that update as your UI changes
• Flexible deployment: test in the cloud, behind a private network, or locally
• Mobile testing features: a suite of AI tools designed specifically for native mobile testing

Does it work? Just ask Best Parents, who expanded their test coverage to 80% in just two days without writing a single line of code. 

Want to join them? Book a demo today with one of our engineers