You're Fixing The Wrong Bugs (And It's Costing You Money)
Summary
Your bug tracker has 47 open errors, but which one is breaking checkout? Which affects just one user? Which is getting worse this week? Most teams debug randomly instead of strategically, wasting time on low-impact bugs while critical issues affect revenue. Without impact scoring and trend analysis, bug backlogs become undifferentiated noise where revenue-critical errors sit alongside cosmetic annoyances.
It's Monday morning. We open the bug tracker and see 47 open issues. Our manager asks: "Which bug should we fix first?", so we scan the list:
TypeError: Cannot read property 'user' of undefined (opened 3 weeks ago)
API timeout in payment processing (opened yesterday)
Button misaligned on mobile Safari (opened 2 months ago)
Which one matters? We check timestamps, read descriptions, look for labels. Nothing tells us which bug is breaking the business and which one is just noise. So we do what every engineering team does: we fix the newest one. Or the loudest one. Or whichever one the CEO just complained about in Slack.
This is the hidden cost of undifferentiated bug backlogs.
The Hidden Cost of Random Debugging
Here's what most engineering teams don't talk about: 40% of developer time goes to debugging. That's not controversial; everyone knows debugging takes time. But here's the part that hurts: 60% of that debugging time is spent on the wrong bugs.
On the face of it, it's easy, right? Oh, the checkout flow has a bug affecting 27 users this week with a revenue impact of $1,000. The admin panel has a typo in a rarely-used modal with a revenue impact of $0. Both are "open bugs" in the same list but surely we tackle the checkout flow first... right? Hands up if your team has ever fixed the admin panel issue first.
The typical mid-size engineering team has 30-50 open bugs at any time. Without impact scoring, we're debugging blind.
Why "Newest" Doesn't Mean "Most Important"
The default sort order in most bug trackers is created date (newest first). This makes sense for support tickets but makes zero sense for engineering priorities.
Consider these three bugs, all opened in the same week:
Bug A: Payment API throwing 500 errors
Started last Tuesday, affecting 5% of transactions. Trending UP from 4 errors/hour to 20 errors/hour. Revenue impact: Medium-High.
Bug B: Search results sometimes empty
Started Wednesday, affects 1 user so far. Not trending. Revenue impact: The CEO saw it.
Bug C: Dashboard loading slowly
Started Monday, affects all users. Trending DOWN (we added a CDN). Revenue impact: Low (UX annoyance, not a blocker).
If we sort by date, we fix Bug A last because it's the oldest. But it's the only one that's actively getting worse and costing money. This is why "newest first" is dangerous. It rewards recency, not urgency.
If we sort by perceived priority, then take a guess at which one we're fixing first.
The "Gut Feeling" Approach Doesn't Scale
Small teams can debug by feel. With 5-10 bugs, someone on the team knows which ones matter. We discuss them in standup and have context. But the moment we hit 15-20 open bugs, gut feeling breaks down.
Why? Cross-project bugs mean different teams don't know each other's context. New team members can't distinguish critical from noise. Distributed teams lack shared standups to build tribal knowledge. Technical debt means old bugs become invisible background radiation.
At 30+ bugs, the backlog becomes write-only. Bugs go in. Nobody reads them. They accumulate until someone does a "bug bankruptcy" and closes everything older than 90 days. We've all done this.
Traditional Bug Trackers Don't Solve This
Let's be honest: Jira, Linear, GitHub Issues don't actually triage bugs. They organize them. They let us add labels and priorities. They sort by arbitrary fields. But they don't tell us which bug affects the most users, which bug is trending worse this week, which bug blocks revenue versus which is cosmetic, or which bug is a known pattern with a 5-minute fix.
We have to manually research every bug to figure out its impact, and we have to do this over and over as new bugs arrive. Most teams don't. Instead, they use proxies:
- "Fix bugs with 'P0' label first" – But who sets the priority? Gut feeling. Or the CEO.
- "Fix bugs affecting premium customers first" – But how do we know which users are affected? Manual correlation.
- "Fix bugs with the most comments first" – So the loudest bug wins? Great.
Traditional bug trackers are error organizers, not error intelligence. They're filing cabinets, not assistants.
The Real Problem: Lack of Context
Here's what we actually need to know to triage a bug:
- Impact: How many users are affected?
- Trend: Is this getting better or worse?
- Severity: Does it block functionality or just annoy?
- Recurrence: Is this a new bug or the same one we've seen before?
- Pattern: Do we have a known fix for this class of error?
Most bug trackers give us none of this. We get a stack trace (if we're lucky), a description (often incomplete), a timestamp, and maybe some labels (inconsistently applied).
So we waste engineering time doing forensics. We search logs to see how many times it occurred. We correlate error IDs to user accounts. We check if this matches a previous bug. We Google the error message for the 47th time. By the time we've gathered context on 5 bugs, an hour has passed and we still haven't fixed anything.
Why This Matters Now
Five years ago, this was just "the cost of doing business." Debugging was manual. Triage was manual. We hired more engineers and hoped for the best.
But something changed: AI coding assistants. Claude, Copilot, Cursor are incredible at fixing bugs once we tell them what to fix. But they can't tell us which bug to fix first. They have no context about production errors, user impact, or business priorities.
We're giving AI tools the keys to the codebase, but they're debugging blind just like we are. This is why the problem is more urgent than ever. AI makes code easier to write but doesn't make bugs easier to triage.
What's Next
If you're a senior engineer or engineering manager, you've lived this problem. You've spent Monday mornings staring at that bug list, wondering what to fix.
The problem isn't volume. It's lack of intelligence. You don't need more bug tracking. You need error intelligence: context, impact scores, patterns, and prioritization.
Next, we'll explore why AI coding assistants make this problem worse, not better, and what needs to change.
This is Post 1 of our launch series on error intelligence. Follow along as we unpack the problem, introduce solutions, and show you how Cazon is building the missing intelligence layer between production errors and AI coding assistants.