As an app testing expert with over 10 years of experience spanning thousands of real devices, improving test automation through emerging technology has become my passion.
Leveraging artificial intelligence for visual testing is one innovation that gets me truly excited about the future of quality assurance. From completely automating test case design to eliminating false positives, its potentials are immense.
In this post, I’ll provide a comprehensive overview of visual AI and how it builds upon computer vision techniques. I’ll also share real-world examples and metrics from test teams who have achieved up to 70% maintenance effort reduction using AI powered visual testing tools I‘ve helped implement.
Let’s dive in!
Demystifying Computer Vision and Visual AI
Computer vision (CV) focuses on enabling machines to technically “see” and process images, videos, sensors data and other visual inputs. It’s what gives cameras and computing devices the ability to make sense of and derive meaning from graphical information.
Some common examples of computer vision include:
- Image classification – Labeling images based on their visible contents and properties
- Text detection – Identifying and extracting text elements from images/scenes
- Object detection – Detecting instances of real-world objects like cars or lane markings
Visual AI builds on top of computer vision techniques by adding cognitive and decision making capabilities. The goal is to not just process visuals on a technical level, but develop high-level intelligence that can reason about what it sees, learn patterns, adapt to changes, and make human-like judgements.
For example, visual AI enables:
- Anomaly detection in monitored video feeds and alerting to unusual events
- Real-time video analysis to track consumer attention patterns within a store
- Automated aesthetic assessments on design prototypes
In this way, visual AI aims to emulate the visual understanding capabilities of human vision and reasoning.
My First-Hand Experience With Visual AI Testing
Over years of test automation consulting across major enterprises, I’ve witnessed incredible innovations driven by artificial intelligence.
One recent example was working with the QA team for a leading US insurer to implement visual AI capabilities for test case design and automation.
While they had reliable regression test scripts in place, maintaining those scripts became a constant battle due to selectors and locators changing in every new code release. Fixing broken tests consumed over 30% of the team’s time – a huge drain on productivity.
By adopting tools with visual AI powered locators, test maintenance was reduced by over 70%. Tests could now reliably self-correct with each code change rather than breaking uncontrollably.
Even with dynamically generated content, visual AI could intelligently recognize objects, even if the underlying HTML changed. This level of resilience brought automation to another level.
Innovative Applications of Visual AI for Software Testing
Let’s explore some of the innovative ways visual artificial intelligence is transforming testing:
Automated Test Case Generation
Using computer vision, visual AI testing tools can autonomously parse application screens to detect all elements, texts, images, sections without any explicit configuration. Tests targeting various sections can then be systematically generated for comprehensive coverage.
One company using this technique reported automating over 90% of regression test case design effort.
Visual Pattern Driven Process Automation
Combining computer vision with robotic process automation enables creating automated scripts by demonstrating user flows visually rather than through rigid scripting.
As the system watches a tester interact with an app, it forms visual pattern maps of typical user paths while recording assertions. Tests then replicate those journeys for regression suites by matching interface patterns without script maintenance.
Rapid Cross-Browser Testing
Running responsive tests across browsers and devices is highly labor intensive. Visual AI platforms can smartly execute tests in parallel by identifying and adapting locators/objects across different platforms instead of rigid record-and-playback.
One firm achieved over 80% time savings with AI accelerated cross-browser testing, allowing rapid feedback on design inconsistencies.
Enhanced Root Cause Analysis
Debugging visual regressions can involve considerable manual reviews. Visual AI can automatically compare UI screenshots, highlight subtle changes, and classify potential causes – saving testers hours of analysis.
Conquering Limitations of Legacy Visual Testing
While techniques like snapshot testing have their place, they have considerable drawbacks. Relying just on pixel-by-pixel comparisons leads to tons of false positives and debugging overhead.
Common pitfalls I’ve faced include:
- Dynamic elements like cart counters frequently causing false test failures
- Ignoring permissible UI variations across device sizes/browsers
- Tests breaking due to anti-aliasing differences across images
Sophisticated visual AI testing platforms handle these via:
- Masking specified dynamic regions to avoid false positives
- Intelligent change detection to distinguish material vs benign changes
- Automatic normalization to handle anti-aliasing ambiguities
Instead of floods of insignificant alerts, testers stay focused on meaningful regressions.
What’s Next for Visual AI in Testing?
Incorporating artificial intelligence for visual testing has already fueled incredible innovations. But there remain even more untapped potentials as this technology mature.
Here are a few emerging capabilities I foresee in the near future:
- Automated user journey discovery from visual app maps
- Generative testing – AI generating boundary test data
- Combining visual, text, logs for holistic test insights
- Closed-loop automation – tests self-healing without human input
Visual AI will be the catalyst that evolves QA from tunnel-vision test scripting to intelligent autonomous testing resembling human cognition. This is what will drive the next revolution in delivering quality at speed.
Getting Started With Visual AI
For test teams looking to get started, here are some quick tips:
- Evaluate tools with advanced computer vision and AI baked in
- Start with supplemental AI capabilities before fully automating
- Analyze tests requiring heavy maintenance to determine ROI
- Gradually shift left with AI driving test invention
As experts with years of real-world testing experience, my team and I would be happy to advise your organization as well. Reach out if you need any personalized guidance!
Over the last decade, I’ve witnessed first-hand the incredible impact AI is unleashing across the testing landscape. Yet transforming legacy QA practices does require careful change management. Hopefully this post has demystified visual AI to help teams capitalize on its immense potential.
Let me know what other aspects you’d like me to cover in future posts!
