From debug debt to developer velocity: How tech teams use Noibu to fix the right bugs, fast

Engineering teams don't have a bug problem. They have a signal problem.

The backlog is full. Every issue looks equally urgent. Half the sprint burns on reproduction — digging through logs, trying to recreate the exact device, browser, and session state that triggered the error — before anyone writes a single line of a fix. And that's assuming the bug gets escalated at all. Most don't. They live on the site for months, silently costing revenue, because no one had enough information to justify picking them up.

The engineering teams that break out of that loop aren’t the ones with more developers. They’re the ones who stopped flying blind — and started using a platform that gives them full visibility across errors, site performance, and the user experience in one place.

The real cost of reproduction

Jared Poole, Technical Support Manager for Digital Commerce at Scrubs & Beyond, remembers the before state with clarity:

"Before Noibu, the whole process of trying to debug and find where in the stack trace was causing issues… that whole process was my life for a few months and it was incredibly frustrating. By the point errors got to me, it had probably been around for months, if not more."
— Jared Poole, Technical Support Manager for Digital Commerce, Scrubs & Beyond

The reproduction problem isn't unique to Scrubs & Beyond. It's the defining constraint of ecommerce engineering at scale. When a customer reports an issue — if they report it at all — they typically can't tell you what browser they were using, what they had in their cart, or what sequence of actions led to the error. That leaves developers guessing.

At Carrefour, one of Europe's largest retailers, the same friction played out across a large engineering team. Jean Philippe Blerot, Head of Digital & Ecommerce Projects, describes what bug resolution looked like before Noibu:

"We could replicate an error, but in order to do that, many of our developers needed to try and replicate the error to confirm its validity. And even if we found and validated a bug, we didn't actually know what needed to be done in order to solve it. I imagine it would take a minimum of one week to understand a bug, and then two weeks to try and solve it. That's just too much time."
— Jean Philippe Blerot, Head of Digital & Ecommerce Projects, Carrefour

Three weeks per bug. For a team running a high-traffic ecommerce estate, that math doesn't work. Errors accumulate faster than they get resolved, the backlog grows, and the team shifts permanently into reactive mode.

What changes when every error arrives developer-ready

Noibu monitors every front-end session and captures the full context of each error the moment it occurs: the stack trace, the browser and device, the user's session replay, the sequence of actions leading up to the failure, and the revenue impact based on how many customers were affected. By the time a developer opens a Noibu ticket, the work of reproduction is already done.

At Store Supply Warehouse, that change was measurable immediately. Tim Haverman, a .NET Software Developer on the team, tracked the before and after directly:

"Since integrating Noibu, the time we spend identifying and resolving errors has drastically reduced. What used to take 10 hours a week can now be addressed in just 2 hours, allowing us to be much more efficient."
— Tim Haverman, C# .NET Software Developer, Store Supply Warehouse

An 80% reduction in time spent. For Haverman's team, that's not just an efficiency metric — it's capacity recovered for work that actually moves the product forward.

Jared Poole at Scrubs & Beyond arrived at the same conclusion from a different angle:

"We could have easily hired someone full time to do what I was doing before. Now I am able to put my time elsewhere because Noibu will do the job for me."
— Jared Poole, Technical Support Manager for Digital Commerce, Scrubs & Beyond

The insight matters beyond the individual time saving. When reproduction overhead is eliminated, engineering capacity doesn't just get freed up — it gets redirected. Teams that were spending half their time chasing context are now spending that time on resolution, testing, and prevention.

Three days to 30 minutes

Suntheng Taing, Senior Software Engineer at Floor & Decor, describes a specific incident that illustrates the shift better than any statistic.

A customer had submitted a design appointment online, shown up for it in person, and found their appointment wasn't in the system. The development team spent three days trying to diagnose the issue — debugging the scheduler logic, checking form submission handlers, looking for defects in the confirmation flow. Nothing surfaced.

They brought the problem to Taing and asked him to use Noibu to investigate:

"I spent 30 minutes, found the user session, saw that the customer did not submit the appointment. They never pressed 'Accept' — therefore they never got a confirmation page. This 3-day endeavour that the development team spent only took me 30 minutes to uncover that it wasn't an issue in the code, it was a human error."
— Suntheng Taing, Senior Software Engineer, Floor & Decor

The resolution wasn't a code fix. It was clarity. The ability to find the exact session, watch what the user actually did, and rule out a development issue in half an hour — that's what session context and full error replay make possible.

Taing's experience extends across teams he works with: "That's honestly the best part of the tool. Being able to help out with different teams — utilizing Noibu has reduced so much time in triaging."

Engineering as a revenue function

The most durable shift Noibu creates for engineering teams isn't speed — it's language. When every error carries a revenue impact figure, technical leads gain something they often struggle to demonstrate: the ability to connect their work directly to business outcomes.

Lauren Herdman, Engineering Manager at Mejuri, describes how this changes her relationship with the product team:

"The big thing of knowing whether it's actually caused by our code or not — and the fact that you have that in your platform with potential revenue impact — is really great. It also helps me be able to go to the PMs and say, 'Hey, this is actually really important.'"
— Lauren Herdman, Engineering Manager, Mejuri

That conversation — engineer to PM, error to business case — used to require gathering data from multiple sources and hoping the case was persuasive enough. With Noibu, the revenue impact is already attached to the ticket.

Serge Moreau, VP of Technology at Tommy John, describes how this shapes sprint planning across his entire engineering org:

"We have a key list of priorities that we set with the business. We meet weekly to understand the business priorities, then we're able to go into our backlog based off of issues that are high-revenue impacting. It's a culmination of solving for what those priorities are, mirroring what we're seeing in Noibu, and then pulling in what we consider technical debt. It allows us to tackle all the things a developer would need to consider — not just focusing on issues, or just business priorities, or just technical debt. Finding that synergy is crucial."
— Serge Moreau, VP of Technology, Tommy John

The framework Moreau describes — issues, business priorities, and technical debt in balance — is what engineering leadership looks like when it's grounded in data. Noibu makes that data available at the sprint level, not just in quarterly reviews.

Fixing errors you didn't know you had

At scale, the reproduction problem compounds. The larger the site, the more third-party integrations, the more device and browser combinations in play — the wider the surface area for errors that never get reported and never get fixed.

Matt Ezyk, Senior Director of Engineering Ecommerce at Hanna Andersson, frames this as a fundamental constraint of growing a digital business:

"As we scale our business, it's important to have tools embedded into the system that allow us to detect errors, because you cannot do this manually at scale. When you're continuing to grow the site, it becomes increasingly more difficult — so a tool like Noibu allows us to detect errors and proactively remediate them at scale."
— Matt Ezyk, Senior Director of Engineering Ecommerce, Hanna Andersson

The regression detection use case is where this becomes especially acute. Every code release carries the risk of breaking something that worked before. Without automated monitoring, regressions surface through customer complaints — often days after the deployment that caused them. Matt Ezyk describes what Noibu changes for his team's release workflow:

"The biggest unlock for my dev team is to be able to detect regressions before they become an issue. When we release code, we know instantly if we've introduced a regression to the site, which is really powerful for us to detect the health of our business."
— Matt Ezyk, Senior Director of Engineering Ecommerce, Hanna Andersson

Instant regression detection on every release. For a team shipping code regularly, that's not a nice-to-have — it's the difference between a deployment being a controlled event and a gamble.

What engineering looks like on the other side

Teams that have made the shift from reactive to proactive describe a consistent set of changes:

• Tickets arrive with full context. Stack trace, session replay, device and browser, revenue impact — everything a developer needs to understand and reproduce the issue is attached from the moment it's detected.
• Reproduction overhead disappears. The hours previously spent reconstructing the conditions of an error are eliminated. Developers spend their time on resolution, not investigation.
• Sprint prioritization becomes data-driven. Revenue impact figures make it straightforward to rank bugs by business value, align with product and ecommerce leadership, and defend sprint decisions with evidence.
• Releases get safer. Regression detection means deployments are monitored in real time. If something breaks, the team knows within minutes — not days.
• Errors that would have lived forever get closed. The most consistent finding across Noibu engineering customers: issues that had been on the site for months, deprioritized because they couldn't be reproduced, surface and get resolved within the first weeks of using the platform.

Dan Goodwin, Frontend Developer at Ruroc, put it as directly as anyone: "In terms of a development experience, I would go as far as to say the tool is invaluable. It's just an awesome tool to pinpoint what's going on."

About Noibu

Noibu is the leading ecommerce analytics & monitoring platform, purpose-built to help retailers protect and grow online revenue. By unifying site monitoring, experience analytics, and conversion growth opportunities in a single pane of glass, Noibu captures the most important end-to-end shopping data, without the complexity of traditional analytics tools. 

Noibu surfaces critical site errors, performance issues, and customer journey friction that block conversions, then ties every insight directly to business impact, session replays, and full technical context. This makes it easy for ecommerce teams to understand why things are happening and what to prioritize, without dedicated analytics headcount.

The result: faster decisions, better collaboration across teams, optimized customer experiences, and revenue growth.