SYN: Guardian of the Production Manifold

Problem Modern software can pass unit tests and staging, yet fail structurally at production scale. An O(n²) path can look harmless on 100 rows and time out on 20,000. A shared resource can behave normally at low concurrency, then hit a capacity cliff as contention grows. Conventional tests validate correctness; load tests often discover these failures after code has merged. Solution SYN is a pre-merge gate that measures how a change behaves as scale and concurrency increase, then fits the laws behind those measurements. It uses two independent lenses: • Complexity: SYN sweeps input size across multiple scales, measures execution cost with repeated samples, and fits a growth exponent with a confidence interval. Superlinear growth is surfaced as WARN or REFUSED according to evidence-backed thresholds. • Stability: SYN measures latency across bounded concurrency levels and fits the Universal Scalability Law (USL). A nested statistical test determines whether the coherency term κ is supported by the data. When a capacity cliff exists, SYN derives its peak, separatrix, and headroom at expected load—without needing to drive the service into failure. The result is PASS, WARN, REFUSED, or UNKNOWN. UNKNOWN is first-class: when evidence is insufficient, SYN does not silently approve the change. Who uses it Backend engineers, developers, and engineering leads add SYN to CI and declare the functions or services to probe in syn.json. On a pull request, GitHub Actions runs the probes, posts the verdict and certificate, and blocks the merge when configured to refuse it. A live dashboard exposes the capacity curve, scaling law, statistical evidence, sensitivity analysis, and risk map. What makes it different SYN does not gate on a heuristic score or a single benchmark. It measures structural behavior and asks whether the observed law is statistically supported. It calibrates a no-op noise floor on the same runner, requires real κ to clear that floor, and refuses to trust a cliff that would require excessive extrapolation beyond the measured range. It also audits itself. During validation, GitHub’s runner exposed a false positive caused by scheduler-level timing noise that was not reproduced on the laptop. The noise-floor calibration and range guard were tightened, and the failure became a regression test. Proof SYN has been blind-validated on 9 anonymized targets, including trap negatives such as linear contention, an N+1 query, and an O(n log n) sort. Across 3 seeds on both a laptop and GitHub’s runner, it achieved 3 true positives, 0 false positives, 6 true negatives, and 0 false negatives per seed. Most importantly, SYN has refused a real GitHub pull request. A one-line change introduced a measurable coherency cost: κ = 1.07e-2, p = 1.7e-6, separatrix N = 14.5, with headroom of 1.23× at the tested operating load. The certificate was posted on the PR. Efficiency A gate run takes about 1–4 minutes on a standard runner and requires no always-on backend. Results are published as static data. SYN moves scaling and stability checks to the cheapest point in the software lifecycle: before merge.