Two things can be true at once. How teams build products is evolving, so software testing strategies also need to evolve to remain effective. Equally, many of the fundamental building blocks of a great testing strategy remain unchanged.
Sure, it’s a brave new world out there, and you’ll need to adapt to thrive but getting the basics right gets you 50% of the way there without even considering the potential offered by newer, AI-based approaches to testing.
And, when it does come to capitalizing on what AI can do, there’s a relatively even split between facilitating existing processes and enabling new ways of working. You are probably not going to move away from regression testing on every commit, but you may want to rethink how many hours your team spends on maintenance, for example.
It’s good to keep on top of the latest software testing trends. It’s even better to understand those trends in the context of existing processes. Here’s how things lie in 2026 and beyond.
What’s Not Changed: The Fundamentals of Great Software Testing Strategies
Clear Quality Goals Are Still Essential
It’s as important as ever to establish what you hope to achieve by testing software, and to monitor your progress towards these goals.
The more precisely aligned your goals are with key business outcomes, the more effective this step will be. Here’s what we mean:
❌ Poorly defined goal: ‘improve software quality’
- Imprecise – this might mean different things to different people
- Difficult to measure – vagueness causes confusion over which metrics to track and whether you’re actually making progress towards the goal
- No business context – confusion as to why you are working towards this
✔️ Well-defined goal: ‘zero critical defects in production’
- Immediate clarity – the goal is easily definable with no scope for misunderstanding
- Easy to measure – you can immediately pick out which metrics would be beneficial here (for example, defect escape rate, mean time to detect)
- Clear business context – correlation between avoiding major defects and user retention
AI tools will make tracking your performance much easier, but they cannot define your goals for you. That’s still completely up to you.
Your Engineers Should Own Code Quality
Regardless of whether there is a QA handoff involved, position quality as something fundamental to the software development lifecycle, rather than an additional concern to be dealt with as a final step before release.
This approach encourages developers to think more critically about edge cases, user journeys, performance, and security while features are being designed and implemented, rather than after the fact. It also creates faster feedback loops, reducing the time and cost associated with fixing defects.
This has always been a core pillar of any effective software testing strategy, but its importance has only increased as engineers test more of their own code , and ‘early and often’ approaches to testing become the norm.
What’s Evolving: Software Testing Strategies Accelerated With AI
Test Creation and Execution Is Faster, So You Can Plan For More Coverage
AI tools allow you to create tests from natural language prompts. They can also execute tests faster, with native parallelization features and CI/CD integration that minimizes the manual effort needed here.
In numbers: Momentic customer Quora run daily test suites in just 30 minutes and automated 100% of critical tests in one month.
This frees up a significant amount of testing time. So, test more. If you can create a test using natural language in 30 seconds, it’s incredibly easy to scale coverage for easy gains in product quality.
You’ll still need to take a risk-based approach if your app is large and complex, and 100% coverage is off the table. You’ll still have significantly increased leeway to run more tests in the same amount of time.
CI/CD Pipelines Are Still Everything But You Can Make Them Faster, More Automated, and More Secure
CI/CD isn’t going anywhere as part of your software testing strategy, but you should avoid assuming you’ve ticked the box already and nothing needs to change.
AI testing technology offers huge potential for accelerated, smarter CI/CD workflows that ease the pressure of handling multiple releases per day, especially for smaller teams. If you’ve not reviewed your CI/CD workflows for a while, here’s where you could take efficiency up a notch:
- Agentic workflows: Many CI/CD tools are introducing agentic workflows that allow teams to describe automation goals in natural language instead of YAML
- AI-driven automation: AI agents can also autonomously create merge requests to repair broken builds, enhancing the automation process
- Cloud-native builds: Pipelines will run on temporary containers, ensuring predictable performance and clean environments
If you’ve not yet shifted to a security-as-code model (as opposed to running security checks as a separate touchpoint before deployment), this could offer further benefits.
Thanks to the rise of AI-assisted code creation , engineering teams generate more code, more services, more infrastructure, and more dependencies than ever before. Security teams haven’t grown at the same pace, so traditional approaches are becoming increasingly difficult to maintain.
Security-as-code allows organizations to scale security alongside software delivery, so you can maintain coverage as demands grow. This has made it increasingly integral to software testing strategies over the past few years – especially for teams who want to futureproof their processes.
What’s New: Software Testing Strategies That Are Just Hitting the Mainstream
Shifting Left and Right: Now Viable For Most Teams
Shifting left (testing earlier in the development cycle when errors are easier and cheaper to fix) and shifting right (real-world production monitoring) aren’t brand new ideas. However, personnel and tooling restraints have relegated them to ‘pipe dream’ status for many teams until recently.
Shifting left and taking a broadly ‘test as you go’ approach is now significantly easier with minimal time spent, thanks to natural language tools and agentic AI technology that explores your app autonomously in the background.
That agentic technology also makes it easier for teams to continuously monitor products post-deployment. Previously, this was difficult for all teams except those with an extremely luxurious amount of manpower. With AI, it’s far more accessible – consider incorporating into your own software testing strategy for visible quality improvements.
The Fundamental Shift: Software Testing Strategies Can Be Proactive, Not Reactive
Until this point, the only way you have been able to test is reactively. You test. Something is broken. You find the thing that is broken and fix it.
AI agents operate and reason autonomously . This allows you to move beyond the ‘find and fix’ paradigm into something more analytical. Your AI tool can identify gaps in coverage, potential high-risk areas, and likely reasons for test failure based on your code and historic test data. So, rather than reacting to test failures, you can anticipate what might go wrong and take preventative action.
This means that software testing strategies can move beyond reacting to errors, and towards fixing high-risk areas before errors occur – which offers huge potential for accelerating release cycles.
…And You Can Win Back Most Of The Time You Spend on Test Maintenance
Until now, all software testing strategies have been built around the fact that test maintenance is time-consuming, and that this will limit you in some way, for example by limiting your ability to scale coverage to the extent you would like.
AI tools like Momentic experience your app like a human would. Rather than relying on brittle selectors like CSS and XPath, they gauge how your page fits together (which button goes where, etc) via intent markers. This means that:
- Tests ‘self-heal’ (update with the UI) rather than flaking
- Your team minimizes the time spent on routine manual maintenance
- You can reinvest hours elsewhere
For teams that ship frequent UI updates, this has the potential to save hundreds of engineering hours per quarter .
Momentic: The Ultimate Tool For Modern Software Testing Strategies
“Momentic changed how we approach frontend testing. Instead of treating it as a bottleneck, it’s now a core part of how we ship changes every day.”
With Momentic, digital trade platform Nuvo scaled to 80% frontend test coverage in 3 days with end-to-end test creation accelerated by 90%, thanks to:
- Natural language test creation: no code needed – write what you want to test in plain English, then execute the test immediately
- Smart self-healing tools with intent-based selectors that update as your UI changes
- Autonomous agentic workflows: Momentic explores your app independently to identify high-risk areas, suggest reasons for test failure, and identify missing edge cases
Want to join them? Get a demo today