Deque Systems announced Axe Pro, a key addition to Axe, the web accessibility testing browser extension.
Axe Pro features enable developers to run both automated and intelligent guided tests against their websites and applications, helping ensure they are accessible to all users, including people with disabilities. Still in beta, Axe Pro is available for free for a limited time and will continue to be improved based on feedback from developers everywhere.
“Axe Pro essentially lets every developer function as an in-house accessibility expert,” says Preety Kumar, CEO of Deque Systems. “It helps developers identify more areas for accessibility optimization, which is important as the rate of digital accessibility lawsuits continues to accelerate.”
Axe Pro builds upon the strengths of Deque’s open source axe accessibility rules engine by enabling developers to conduct intelligent guided tests, based on simple question-answer interactions that don’t require additional accessibility expertise. Available through a web portal within Axe, Axe Pro escorts developers through inspections of their websites and applications and returns reports highlighting areas for optimization. For example, Axe Pro will highlight images where the accompanying alt text may not match the image.
Developers can typically identify about half of all critical accessibility blockers through Axe. Augmenting the use of Axe with additional Pro features will help developers increase their testing coverage with no additional resources needed.
“Enlisting the participation of the developer and accessibility community has always been a hallmark of Deque’s approach. It’s the reason we have open-sourced our axe rules libraries and also the reason we are soliciting developer input on these new additions to axe,” continues Kumar. “This feedback is essential in our efforts to continually enrich and expand our offerings, helping developers implement accessibility comprehensively, confidently and easily.”
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...