Bugs are a fact of software life. But prolonged bugs? That’s a failure in systems thinking.
At Verbat, we’ve operationalized a consistent, reliable approach that enables us to detect, triage, and resolve most bugs in under 36 minutes—without cutting corners. This isn’t about hustle; it’s about process maturity, observability, and smart automation.
Here’s how we built a rapid-response bug-fixing engine—one that scales with quality and efficiency.
1. We Design for Debuggability
Fast bug fixes start long before anything breaks.
From the architecture level, we embrace Design for Debuggability principles. We deliberately build clean module boundaries, establish fail-fast behaviors, and implement verbose but structured logging. This ensures any future issue has a narrow scope and an immediate digital footprint.
We don’t treat debuggability as an afterthought—it’s part of our software design checklist.
2. Real-Time Observability That Doesn’t Miss a Beat
Detection is often the silent killer in bug resolution. A delay in discovery is a delay in delivery.
Our applications are monitored in real time with an integrated stack of crash tracking, performance monitoring, tracing, and centralized logs. From frontend errors in production to backend API timeouts, every anomaly is flagged within seconds.
Our telemetry includes contextual breadcrumbs: which user action caused the issue, what data was passed, which services were triggered, and where exactly things went off-track. We never chase vague “something broke” reports—only actionable, data-rich alerts.
3. Instant, Zero-Overhead Triage
When an error occurs, there’s no waiting for a project manager to create a ticket or organize a sync-up.
Our alerting pipelines auto-tag bugs by environment, severity, and ownership. Responsible developers are pinged immediately, with error context already bundled. Triage happens in real-time, usually within 90 seconds of detection.
By reducing manual handoffs, we collapse lag between discovery and action.
4. Reproducibility Is Engineered, Not Assumed
Most time is lost not in fixing the bug, but in finding how to reproduce it.
We’ve solved this with reproducibility frameworks baked into our dev and QA tooling. Every error is automatically logged with environmental metadata, API traces, and input states. If needed, we capture screen recordings or DOM snapshots using headless browsers.
Reproduction takes minutes—not hours. No back-and-forth with clients. No guesswork.
5. Fixes Are Isolated and Safe to Deploy
Once a fix is in, our pipelines take over. Engineers commit code into dedicated branches with automated linting, testing, and build validation steps. We use feature flags and progressive deployment (blue-green or canary) to minimize exposure.
The goal is to merge confidently, not hastily.
In most cases, production deployment happens within 20–25 minutes after the fix is written, fully tested, and reviewed.
6. We Capture Every Fix—And Learn From It
After a fix is deployed, it’s never just marked “done.” We record it in our internal Bug Intelligence Repository—a curated knowledge base of root causes, contributing factors, and mitigation patterns.
Each entry is tagged to help us recognize recurring fragilities. These insights guide future architectural decisions, improve test suites, and reduce noise in monitoring.
We’re not just fixing bugs. We’re strengthening the system each time.
7. Speed Is Baked Into Culture—Not Just Process
Fixing bugs quickly isn’t just a technical challenge; it’s a cultural advantage.
At Verbat, every developer is accountable for MTTR (Mean Time to Resolution). We highlight fast responses, track improvement trends, and continuously refine our SDLC to remove friction. Our engineering culture rewards clarity, speed, and preventive thinking.
The result? A team that doesn’t fear bugs—they fix them, fast.
In Closing
A slow bug fix costs more than just developer time—it damages user trust, disrupts business flow, and hurts brand credibility.
At Verbat, our 36-minute bug resolution model isn’t a fluke. It’s the product of deeply integrated systems, engineered reproducibility, smart automation, and a culture of continuous improvement.
If your current engineering team treats bugs as blockers, maybe it’s time to work with one that treats them as learning loops—and closes them before they make an impact.
