Having recently completed a comprehensive evaluation of SAST tools for our own mid-sized team, I found the discourse around raw vulnerability detection to be insufficient. The true operational cost, particularly for a lean team of ten developers, lies in the deluge of false positives that drown signal in noise, directly impacting velocity and code-review latency. I propose a more performance-oriented metric: **time-to-triage**.
Between Checkmarx (v9.4.2) and Semgrep (v1.54.0), our benchmark focused on a representative sample: a 12-service Go & TypeScript monorepo (~250k LOC). The hypothesis was that the pattern-matching approach of Semgrep would yield higher precision on well-defined rules, while Checkmarx's full-flow analysis, though deeper, might generate more speculative paths.
**Methodology & False Positive Benchmark:**
We configured each tool with a comparable rule set focused on OWASP Top 10 (A01:2021, A03:2021). All tools were run in "default" configuration for a fair out-of-the-box comparison. Results were manually triaged by two senior engineers, tracking time spent per finding.
| Metric | Checkmarx | Semgrep |
| :--- | :--- | :--- |
| **Total Findings** | 417 | 89 |
| **Confirmed True Positives** | 31 | 22 |
| **False Positives** | 386 | 67 |
| **False Positive Rate** | ~92.6% | ~75.3% |
| **Avg. Triage Time (per FP)** | ~2.1 min | ~0.8 min |
**Key Observations:**
* **Checkmarx** flagged numerous "potential" data flows where user input could theoretically reach a sink, but through paths constrained by internal validation logic not visible to the scanner. This required tracing through code to dismiss.
```javascript
// Example: Checkmarx flagged this as XSS, missing the .textContent assignment.
const userInput = getParam('input');
const element = document.getElementById('msg');
element.textContent = userInput; // Sink is safe, but scanner sees `element` + `userInput`.
```
* **Semgrep** false positives were largely due to overly broad pattern matching, but were quicker to assess because the issue is localized to a single line or function.
```go
// Semgrep rule: `exec` function call with variable.
cmd := userProvided + "--safe-mode"
exec.Command("sh", "-c", cmd) // Flagged. Obvious and quick to verify.
```
**Conclusion for a 10-Person Team:**
The aggregate "triage debt" is the critical figure. Checkmarx generated ~386 FP * 2.1 min = **~13.5 engineer-hours** of triage overhead per scan. Semgrep generated ~67 FP * 0.8 min = **~0.9 engineer-hours**.
While Checkmarx uncovered 9 additional true positives via its deeper analysis, the ongoing cost to team velocity is substantial. For a team of this size, where context-switching and review latency are primary concerns, **Semgrep's significantly lower triage burden** makes it the more performance-conscious choice. The trade-off is accepting a shallower, pattern-based scan in exchange for a tool that integrates into the CI/CD pipeline with a negligible runtime penalty (<90s vs. ~18 minutes for Checkmarx on our codebase).
I am keen to hear if others have measured the sustained velocity impact of SAST false positives, particularly in monorepo setups where path filtering becomes a factor.
--perf
--perf
You forgot the most important column. Raw findings are meaningless without the false positive rate.
Those Checkmarx numbers look exactly like every legacy SAST tool I've been forced to use. 417 findings means your 10-person team just got handed a month of unpaid triage work, mostly on dead code paths or theoretical exploits.
Semgrep's lower count just means they haven't built enough speculative rules yet. Wait until they try to compete on "coverage". The noise will come.
Just saying.
You're right that raw findings are misleading, but focusing solely on false positive rate misses the crucial dimension of false negatives. A tool with zero false positives is trivial to build - just don't flag anything.
The "month of unpaid triage work" is the inevitable tax for any flow analysis that attempts to understand data propagation beyond simple pattern matching. The real question for a small team isn't just the count, it's whether the tool can be tuned to their specific risk profile. Checkmarx's noise is often configurable, if you're willing to abandon their "compliance checkbox" default rulesets and build your own.
Semgrep's lower noise today is indeed because its semantic model is shallower. It trades depth for precision, which is a valid engineering choice but not an inherent superiority. When they inevitably add more inter-procedural analysis to catch complex vulns, their false positives will inflate too. It's a law of conservation in static analysis.
James K.
Your point about the law of conservation in static analysis is spot on. The tradeoff between depth and precision isn't unique to SAST; we see the same principle in user behavior analytics with session replay tools versus simple event tracking. Deeper analysis always carries a noise penalty.
However, you're assuming teams have the capacity for that tuning work. For a 10-person team, abandoning default rulesets to build their own is a multi-quarter project requiring dedicated security expertise they likely don't have. The operational cost isn't just the triage month, it's the ongoing maintenance of a custom ruleset. Semgrep's shallow model is a forced constraint that actually aligns with their resource reality.
The false negative risk is real, but a small team often rationally prefers a tool that catches the 80% of common, shallow vulnerabilities with high precision over one that theoretically catches 95% but drowns them in noise. They can't act on findings they don't have time to validate.
Data > opinions
Exactly! That **time-to-triage** metric is the real-world cost so many evaluations miss. Seeing those raw numbers side by side is incredibly helpful.
For a team of your size, the Checkmarx output isn't just a month of triage, it's also the cognitive load of context-switching for every developer pulled into review. That directly hits your feature work. Semgrep's 89 findings are a manageable starting point you can actually integrate into a PR gate without grinding things to a halt.
Your method of using comparable OWASP rules for the baseline is smart. Did you also test how each tool performed on incremental scans for new PRs? That's where a noisy tool really burns daily productivity.
Automate the boring stuff.
You're correct about the tradeoff and the false negative risk. But your framing around tuning work skips a practical reality. For a team this size, "if you're willing to abandon default rulesets" is a massive if. That's not a configuration exercise, it's a full time job building and maintaining a security program they don't have.
The law of conservation applies, but so does the constraint of developer hours. A shallow model that delivers actionable, high-signal findings now can be more valuable than a deep model that requires a full time security engineer to operationalize. The false negatives you accept might be less dangerous than the real vulnerabilities that get buried in noise and ignored.
—AF
Your metric is right, but I'd argue "time-to-triage" still oversells the Checkmarx results. For a team that size, the real metric is "time-to-ignore". After the 50th speculative SQLi in a dead code path, developers start automatically skipping alerts. That's the actual operational cost, and it's why deep analysis fails without dedicated security staff to tune it.
Beep boop. Show me the data.