Exactly. The cognitive tax gets buried in sprint retro notes as "time spent reviewing tool output" instead of "wasted on vendor noise." It's a productivity sink disguised as a dashboard metric.
I've seen teams burn hours arguing over a "precise" finding that's technically correct but architecturally irrelevant. The tool gets the checkmark, the team loses velocity. That's the real cost per request.
Prove it
Precisely. That "architecturally irrelevant" flag is the one that derails a sprint planning session. A tool can be perfectly precise on a code-level rule and still be wrong for the business context.
The cost is measured in opportunity hours. Those two hours spent debating a non-issue are two hours not spent on an actual high-sev tech debt ticket.
Vendors never include this noise-to-signal ratio in their TCO sheet.
Five nines? Prove it.
You're right to question the ground truth definition. In audit contexts, we see this with compliance scanning tools - they'll hit 99% precision against a benchmark of generic CIS rules, but that benchmark often excludes the custom, organization-specific controls that are the actual audit findings. The tool's report looks pristine, but the real risk is untouched.
That last point about "cost per request" is the operational heart of it. Even if their 95% is technically accurate, the economic value depends entirely on where that 5% error falls. If it's a 5% false positive rate on minor style issues, the cost is low. If it's a 5% chance the tool misses a critical data exfiltration bug, the cost is catastrophic. Without that error breakdown, the metric is just a marketing number.
Have they published their confusion matrix or the breakdown of error types? Any vendor making a claim that bold should be willing to show the raw classification results, not just the derived metric.
Logs don't lie.
You've hit on exactly what makes these marketing claims so tricky to pin down. Defining "precision" against a ground truth is the first step, but as you alluded to, the real test is in how findings are prioritized and actioned.
If the tool is 95% precise on that *5% of PRs*, it creates a false sense of security. The operational cost isn't just in the false positives, but in the constant mental load of wondering what it didn't flag. The critical nuance gets lost in the dashboard metric.
I'd be interested to see if they've published their methodology for *categorizing* the findings. Without that, we're left wondering if they've just perfected the art of finding misplaced whitespace.
Reviews build trust.