From Complexity to Confidence: The Smart Approach to Testing Smart Devices
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Optimize your risk-based testing with AI power.

Risk-Based Testing is an approach that focuses testing efforts on areas of a system that pose higher risks to the project's success. It involves identifying potential risks, assessing their impact and likelihood, and prioritizing testing activities accordingly. By allocating resources based on risk, it ensures efficient test coverage and maximizes the likelihood of detecting critical defects. This approach allows organizations to mitigate risks early, make informed decisions, and deliver high-quality software that meets customer expectations while optimizing testing efforts and resources.

Risk-Based Testing leverages artificial intelligence (AI) technologies to enhance the risk-based testing approach. It utilizes machine learning algorithms to analyze vast amounts of data, including historical testing results, defect patterns, and project metrics. Automating risk assessment and prioritization enables more accurate identification of high-risk areas, optimizing testing efforts and resources for efficient and effective testing. Risk-Based Testing AI improves decision-making accelerates defect detection, and enhances overall testing outcomes, leading to higher-quality software products.

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Manual solutions work. Our AI-powered solutions work best.

Risk-based testing done by humans is effective, but adding machine learning, algorithms and automation can bring stunning results.

Bye-bye bias.

Drive selection and prioritization with data, not tester instinct or experience, to improve accuracy and reliability.

Fewer tests, less cost, greater efficiency.

Run fewer tests by identifying those most likely to fail and those that are redundant. Our tools have proven up to 80 percent accurate in predicting test failure.

Shift-left risk elimination.

AI tools can identify the highest-risk, highest-impact areas early so you can put them on the top of the list.

What’s bugging your users? Find out fast.

Data analytics can zip through massive volumes of user feedback, scanning for repeated words and patterns that show how users feel and where they’re spending time on your site. Gain actionable insights to improve your E2E UX.

Quality at speed.

In a DevOps/Agile world of hourly releases, manual testing simply can’t keep up. You need machine learning, algorithms and automation to keep on schedule while ensuring high quality. AIs work around the clock.

Free your testers for bigger things.

AI automates the tedious, repetitive aspects of testing so testers can turn their talents to complex problems and niche or “edge” cases.

We can’t wait to meet you.

We’re eager to help you optimize your testing program. Please complete the contact form below so we can start getting to know you.