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nyx/tests/eval_corpus/tabulate.py

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#!/usr/bin/env python3
"""
Tabulate nyx scan results against a ground-truth file.
For OWASP / SARD sets: compares nyx findings against known-true/known-false
labels from the ground truth JSON.
For in-house sets (--inhouse): counts findings by cap x language; reports
Unsupported rate only (no ground truth required).
Output: appends a result record to --append FILE.
Phase 29 (Track I) extensions:
--budget tests/eval_corpus/budget.toml enforce per-cell budget thresholds
--diff previous.json compare against prior result file,
fail on monotonic-improvement
regression
Exit codes:
0 all rows pass.
2 one or more per-cell budgets exceeded OR a diff regression was found.
3 malformed budget / diff input (callers must fix configuration).
"""
import argparse
import json
import os
import sys
from collections import defaultdict
from pathlib import Path
try:
import tomllib # Python 3.11+
except ModuleNotFoundError: # pragma: no cover — older interpreters only
import tomli as tomllib # type: ignore[no-redef]
LINE_TOLERANCE = 5
# Caps with no sound runtime oracle (config / usage smells) and the catch-all
# `other` bucket route to Unsupported by design, so their Unsupported-rate is
# report-only, never gated. Mirrors report.py / the budget.toml intent.
NO_SOUND_ORACLE_CAPS = {"auth", "crypto", "xss", "trustbound", "other"}
def _soft_unsupported() -> bool:
"""True when the per-cell Unsupported-rate budget is report-only.
CI sets `NYX_EVAL_SOFT_UNSUPPORTED` because dynamic confirmation is
environment-constrained there (the budget is calibrated on a dev box where
confirmation runs fully); the precision / confirmed-rate ratchets stay
hard. Unset (local dev) keeps the Unsupported budget hard.
"""
return os.environ.get("NYX_EVAL_SOFT_UNSUPPORTED", "").strip().lower() in (
"1",
"true",
"yes",
"on",
)
# Bitflag positions for Cap (src/labels/mod.rs). Sink bits map to a cap label.
_CAP_BIT_TABLE = [
(1 << 5, "path_traversal"), # FILE_IO
(1 << 6, "fmt_string"),
(1 << 7, "sqli"), # SQL_QUERY
(1 << 8, "deserialize"),
(1 << 9, "ssrf"),
(1 << 10, "cmdi"), # CODE_EXEC
(1 << 11, "crypto"),
(1 << 12, "unauthorized_id"),
(1 << 13, "data_exfil"),
(1 << 14, "ldap_injection"),
(1 << 15, "xpath_injection"),
(1 << 16, "header_injection"),
(1 << 17, "redirect"), # OPEN_REDIRECT
(1 << 18, "xss"), # SSTI (template_injection); also covers XSS sinks
(1 << 19, "xxe"),
(1 << 20, "prototype_pollution"),
# HTML_ESCAPE (1<<1) is the universal reflected-XSS *sink* cap across every
# language (`grep 'Sink(Cap::HTML_ESCAPE)' src/labels/` — PHP echo, JS
# innerHTML, Java servlet writers, etc.); the same bit is the html-escape
# *sanitizer* cap, so a finding only carries it as a sink when an un-encoded
# tainted value reached an HTML output. Placed LAST so any higher-priority
# sink bit (SQL_QUERY, FILE_IO, ...) on the same finding wins; a finding
# carrying only HTML_ESCAPE is reflected XSS. Without this, every
# taint-based reflected-XSS finding mis-buckets to "other".
(1 << 1, "xss"),
]
# Static lens (see --static): SHELL_ESCAPE (1<<2) is the command-injection sink
# cap for *every* language (`grep SHELL_ESCAPE src/labels/` — all Sink uses are
# command-exec; CODE_EXEC=1<<10 is the eval/code-exec variant, also cmdi). In a
# normal `nyx scan` (no dynamic confirmation) a Java cmdi finding carries only
# SHELL_ESCAPE; the SHELL_ESCAPE→CODE_EXEC remap that buckets it as cmdi is gated
# on VerifyStatus::Confirmed (src/commands/scan.rs), so with 0 confirmations the
# default table leaves these in "other" and the cmdi cell reads 0/0/N. The
# static lens appends SHELL_ESCAPE→cmdi at the LOWEST priority (after every other
# bit) so a SHELL_ESCAPE-only finding buckets as cmdi while a finding that also
# carries a higher-priority sink bit (e.g. FILE_IO) keeps its existing bucket.
# Opt-in via --static so the default confirmed-recall bucketing is byte-identical.
_CAP_BIT_TABLE_STATIC = _CAP_BIT_TABLE + [(1 << 2, "cmdi")] # SHELL_ESCAPE
# Substring → cap lookup for rule IDs. Order matters: most specific first.
_CAP_RULE_TABLE = [
("path_traversal", "path_traversal"),
("sql", "sqli"),
("xss", "xss"),
("ssrf", "ssrf"),
("cmdi", "cmdi"),
("cmd_exec", "cmdi"),
("code_exec", "cmdi"),
("deser", "deserialize"),
("unserialize", "deserialize"),
("redirect", "redirect"),
("xxe", "xxe"),
("template", "xss"),
("auth", "auth"),
("memory", "memory"),
("crypto", "crypto"),
("data-exfil", "data_exfil"),
("data_exfil", "data_exfil"),
("header", "header_injection"),
]
def load_json(path: str) -> object:
with open(path) as f:
return json.load(f)
def cap_of(finding: dict, static_lens: bool = False) -> str:
# 1. Prefer evidence.sink_caps bitmask — the engine's own classification.
ev = finding.get("evidence", {}) or {}
sink_caps = ev.get("sink_caps")
if isinstance(sink_caps, int) and sink_caps:
table = _CAP_BIT_TABLE_STATIC if static_lens else _CAP_BIT_TABLE
for bit, name in table:
if sink_caps & bit:
return name
# 2. Fall back to rule id substring (e.g. py.cmdi.os_system, java.deser.readobject).
rid = (finding.get("id") or "").lower()
head = rid.split(" ", 1)[0]
for needle, cap in _CAP_RULE_TABLE:
if needle in head:
return cap
return "other"
def lang_of(finding: dict) -> str:
path = finding.get("path", "")
ext_map = {
".py": "python", ".js": "javascript", ".ts": "typescript",
".java": "java", ".go": "go", ".php": "php", ".rb": "ruby",
".rs": "rust", ".c": "c", ".cpp": "cpp",
}
for ext, lang in ext_map.items():
if path.endswith(ext):
return lang
return "unknown"
def _norm_path(p: str) -> str:
return p.replace("\\", "/")
def path_matches(gt_path: str, finding_path: str) -> bool:
"""True when a ground-truth path refers to the same file as a finding path.
Ground-truth paths are stored *relative to the corpus root* so the checked-in
JSON stays portable, while nyx emits absolute paths rooted at wherever the
corpus was cloned. Match on a path-component-aligned suffix so the relative
GT path matches the absolute finding path (and the reverse, to keep a legacy
absolute GT file working). Exact equality is the fast path; the `/` boundary
stops `.../BenchmarkTest1.java` from matching `.../xBenchmarkTest1.java`.
"""
g = _norm_path(gt_path)
f = _norm_path(finding_path)
return g == f or f.endswith("/" + g) or g.endswith("/" + f)
# ── Budget loading ──────────────────────────────────────────────────────────
def load_budget(path: str) -> dict:
"""Parse a budget.toml file.
Returns a dict::
{
"default": {"unsupported_rate": 0.8, "false_confirmed_rate": 0.02,
"repro_stability": 0.95, "ratchet_deadline": "..."},
"cells": {(cap, lang): {...overrides...}, ...},
}
Raises SystemExit(3) on a malformed file.
"""
try:
with open(path, "rb") as f:
raw = tomllib.load(f)
except FileNotFoundError:
print(f"ERROR budget file not found: {path}", file=sys.stderr)
sys.exit(3)
except tomllib.TOMLDecodeError as e:
print(f"ERROR budget file malformed: {path}: {e}", file=sys.stderr)
sys.exit(3)
default = raw.get("default", {}) or {}
cells = {}
for row in raw.get("cell", []) or []:
cap = row.get("cap")
lang = row.get("lang")
if not cap or not lang:
print(
f"ERROR budget cell missing cap/lang: {row!r}", file=sys.stderr
)
sys.exit(3)
cells[(cap, lang)] = row
return {"default": default, "cells": cells}
def budget_for_cell(budget: dict, cap: str, lang: str) -> dict:
"""Merge cell-specific overrides on top of [default]."""
merged = dict(budget.get("default", {}) or {})
cell = budget.get("cells", {}).get((cap, lang))
if cell:
merged.update({k: v for k, v in cell.items() if k not in ("cap", "lang")})
# Fall back to a wildcard override if present.
if not cell:
wildcard = budget.get("cells", {}).get((cap, "*")) or \
budget.get("cells", {}).get(("*", lang)) or \
budget.get("cells", {}).get(("*", "*"))
if wildcard:
merged.update({k: v for k, v in wildcard.items() if k not in ("cap", "lang")})
return merged
def enforce_budget(cells: list, budget: dict) -> list:
"""Return a list of human-readable failure strings.
Each cell's measured Unsupported / false-Confirmed / repro-stability
rate is compared against its merged budget row. A missing measurement
(e.g. no Confirmed findings → false-Confirmed denominator = 0) is
treated as "no data" and skipped, never as a failure.
"""
failures = []
soft_unsupported = _soft_unsupported()
for c in cells:
b = budget_for_cell(budget, c["cap"], c["lang"])
if not b:
continue
cap, lang = c["cap"], c["lang"]
max_unsup = b.get("unsupported_rate")
max_false = b.get("false_confirmed_rate")
min_stable = b.get("repro_stability")
min_confirmed = b.get("confirmed_rate")
if isinstance(max_unsup, (int, float)) and c.get("total", 0) > 0:
if c["unsupported_rate"] > max_unsup:
# No-sound-oracle caps (and `other`) are report-only by design;
# the rest are report-only when dynamic confirmation is known to
# be environment-constrained (NYX_EVAL_SOFT_UNSUPPORTED, set by
# CI). Hard otherwise so local dev still ratchets coverage.
line = (
f" {cap}/{lang}: Unsupported {c['unsupported_rate']*100:.1f}%"
f" > budget {max_unsup*100:.1f}%"
)
if not (cap in NO_SOUND_ORACLE_CAPS or soft_unsupported):
failures.append(f" FAIL{line}")
if isinstance(min_confirmed, (int, float)) and c.get("total", 0) > 0:
rate = c.get("confirmed", 0) / c["total"]
if rate < min_confirmed:
failures.append(
f" FAIL {cap}/{lang}: Confirmed {rate*100:.1f}%"
f" < budget {min_confirmed*100:.1f}%"
)
if isinstance(max_false, (int, float)) and c.get("confirmed", 0) > 0:
rate = c.get("wrong_confirmed", 0) / c["confirmed"]
if rate > max_false:
failures.append(
f" FAIL {cap}/{lang}: false-Confirmed {rate*100:.1f}%"
f" > budget {max_false*100:.1f}%"
)
# Repro stability is only enforced when callers stamped at least
# one `replay_stable: true` flag — otherwise stable_replays == 0
# is indistinguishable from "we did not measure stability for
# this row" and the gate would fire vacuously on every clean run.
if (
isinstance(min_stable, (int, float))
and c.get("confirmed", 0) > 0
and c.get("stable_replays", 0) > 0
):
rate = c["stable_replays"] / c["confirmed"]
if rate < min_stable:
failures.append(
f" FAIL {cap}/{lang}: repro stability {rate*100:.1f}%"
f" < budget {min_stable*100:.1f}%"
)
return failures
# ── Diff loading ────────────────────────────────────────────────────────────
def load_previous_cells(path: str, label: str) -> dict:
"""Index a previous results file by (cap, lang) → cell.
The previous file is the same shape as `--append`'s output. We pick the
record whose `label` matches the current run; if no exact match, fall
back to the first record. Missing/unreadable files exit 3.
"""
try:
with open(path) as f:
data = json.load(f)
except FileNotFoundError:
print(f"ERROR diff file not found: {path}", file=sys.stderr)
sys.exit(3)
except json.JSONDecodeError as e:
print(f"ERROR diff file malformed: {path}: {e}", file=sys.stderr)
sys.exit(3)
records = data if isinstance(data, list) else [data]
chosen = None
for r in records:
if r.get("label") == label:
chosen = r
break
if chosen is None and records:
chosen = records[0]
if not chosen:
return {}
return {(c["cap"], c["lang"]): c for c in chosen.get("cells", [])}
def diff_regressions(cells: list, prev: dict) -> list:
"""Compare current cells against previous. Returns failure strings.
Three monotonicity rules:
* Unsupported% must not increase.
* False-Confirmed% must not increase.
* Repro-stability% must not decrease.
Cells absent from `prev` are treated as new (skipped).
A small epsilon (0.5 percentage points) absorbs flake noise.
"""
EPS = 0.005
failures = []
for c in cells:
key = (c["cap"], c["lang"])
old = prev.get(key)
if not old:
continue
# Unsupported.
old_unsup = old.get("unsupported_rate", 0.0)
new_unsup = c.get("unsupported_rate", 0.0)
if new_unsup > old_unsup + EPS:
failures.append(
f" REGRESSION {key[0]}/{key[1]}: Unsupported"
f" {old_unsup*100:.1f}% → {new_unsup*100:.1f}%"
)
# False-Confirmed.
old_conf = old.get("confirmed", 0)
old_false = (old.get("wrong_confirmed", 0) / old_conf) if old_conf else None
new_conf = c.get("confirmed", 0)
new_false = (c.get("wrong_confirmed", 0) / new_conf) if new_conf else None
if old_false is not None and new_false is not None and new_false > old_false + EPS:
failures.append(
f" REGRESSION {key[0]}/{key[1]}: false-Confirmed"
f" {old_false*100:.1f}% → {new_false*100:.1f}%"
)
# Repro stability (higher is better).
old_stable = (
(old.get("stable_replays", 0) / old_conf) if old_conf else None
)
new_stable = (
(c.get("stable_replays", 0) / new_conf) if new_conf else None
)
if (
old_stable is not None
and new_stable is not None
and new_stable < old_stable - EPS
):
failures.append(
f" REGRESSION {key[0]}/{key[1]}: repro stability"
f" {old_stable*100:.1f}% → {new_stable*100:.1f}%"
)
return failures
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("--label", required=True)
p.add_argument("--scan", required=True, help="nyx scan --format json output")
p.add_argument("--ground-truth", default="", help="ground truth JSON")
p.add_argument("--inhouse", action="store_true")
p.add_argument("--append", required=True, help="results accumulator JSON")
p.add_argument(
"--manual-triage",
default="",
help=(
"path to a manual-triage JSON file (list of "
"{path, line, cap, vuln: bool}). Confirmed findings matching a "
"`vuln: false` entry are stamped with `wrong: true` before "
"tabulation so the per-cell False-Confirmed budget becomes "
"non-vacuous without depending on the host's `nyx verify-feedback` "
"log. Matching uses LINE_TOLERANCE (=5) — line == 0 in the triage "
"entry matches any line."
),
)
p.add_argument(
"--budget",
default="",
help="path to budget.toml (per-(cap,lang) thresholds)",
)
p.add_argument(
"--lang",
default="",
help=(
"comma-separated language allowlist (python, javascript, php, "
"ruby, go, rust, ...). When set, only findings AND ground-truth "
"entries whose source language is in the list are tabulated; "
"everything else is dropped before tallying. Used by the Phase 29 "
"polyglot corpora (Track R.2) to scope a single-language corpus to "
"its target language so incidental third-party assets in other "
"languages — e.g. the vendored JavaScript a Rails or aiohttp app "
"bundles — do not pollute that corpus's per-cap metrics. Empty = "
"no language filter (every finding tabulated, the OWASP/JSTS "
"default)."
),
)
p.add_argument(
"--diff",
default="",
help="path to a previous results JSON; fail on monotonic-improvement regression",
)
p.add_argument(
"--static",
action="store_true",
help=(
"static lens: bucket SHELL_ESCAPE (1<<2) findings as cmdi even when "
"they are unconfirmed. Java (and other) command-exec sinks carry "
"SHELL_ESCAPE and only get remapped to CODE_EXEC on dynamic Confirm; "
"without this flag, an env with 0 confirmations reads the cmdi cell "
"as 0/0/N regardless of static quality. SHELL_ESCAPE is the "
"command-injection sink cap for every language, so this is sound "
"globally; it is opt-in only so the default confirmed-recall "
"bucketing stays byte-identical."
),
)
args = p.parse_args()
lang_filter = {l.strip() for l in args.lang.split(",") if l.strip()}
scan_data = load_json(args.scan)
findings = scan_data if isinstance(scan_data, list) else scan_data.get("findings", [])
# Score only Security-category findings against the security ground truth.
# Reliability defects (resource leaks, error-handling fallthrough) and
# Quality findings are real bugs but not the injection / crypto / auth
# vulns the corpus ground truth enumerates, so counting them as security
# false-positives is a category error that wrecks precision with pure
# noise. Findings with no explicit category (legacy fixtures) default to
# Security and are kept.
findings = [
f for f in findings
if f.get("category", "Security") not in ("Reliability", "Quality")
]
if lang_filter:
findings = [f for f in findings if lang_of(f) in lang_filter]
# ── Manual-triage stamping (Phase 31 follow-up) ───────────────────────
# Cross-reference Confirmed rows against a manual-triage file before
# tabulation. Each `vuln: false` entry whose `(path, cap)` matches a
# Confirmed finding (with LINE_TOLERANCE, or any line when triage
# entry's `line == 0`) stamps `wrong: true` on the finding's
# `dynamic_verdict`, which the existing wrong_confirmed counter picks
# up below. Decouples the False-Confirmed budget from the host-local
# `nyx verify-feedback` log so CI on a fresh eval corpus can still
# gate the headline target.
if args.manual_triage and Path(args.manual_triage).exists():
triage = load_json(args.manual_triage)
not_vuln: list[dict] = []
for entry in triage if isinstance(triage, list) else []:
if entry.get("vuln") is False:
not_vuln.append({
"path": entry.get("path", ""),
"line": entry.get("line", 0),
"cap": entry.get("cap", ""),
})
used: set[int] = set()
for f in findings:
ev = f.get("evidence") or {}
dv = ev.get("dynamic_verdict") or {}
if dv.get("status") != "Confirmed":
continue
f_path = f.get("path", "")
f_line = f.get("line", 0)
f_cap = cap_of(f, static_lens=args.static)
for idx, entry in enumerate(not_vuln):
if idx in used:
continue
if (path_matches(entry["path"], f_path)
and entry["cap"] == f_cap
and (entry["line"] == 0
or abs(entry["line"] - f_line) <= LINE_TOLERANCE)):
used.add(idx)
dv["wrong"] = True
ev["dynamic_verdict"] = dv
f["evidence"] = ev
break
# Per-cell tallies: {(cap, lang): {tp, fp, fn, unsupported, confirmed,
# partially_confirmed, wrong_confirmed, stable_replays, total}}
cells: dict[tuple[str, str], dict] = defaultdict(
lambda: {
"tp": 0,
"fp": 0,
"fn": 0,
"unsupported": 0,
"confirmed": 0,
"partially_confirmed": 0,
"wrong_confirmed": 0,
"stable_replays": 0,
# Confirmed-verdict precision/recall accounting, ground-truth-derived
# (only populated when --ground-truth is supplied): confirmed_tp =
# Confirmed findings that match a GT positive; confirmed_fp =
# Confirmed findings that match no GT positive (false confirms).
"confirmed_tp": 0,
"confirmed_fp": 0,
"total": 0,
}
)
for f in findings:
cap = cap_of(f, static_lens=args.static)
lang = lang_of(f)
key = (cap, lang)
ev = f.get("evidence", {}) or {}
dv = ev.get("dynamic_verdict") if ev else None
cells[key]["total"] += 1
if dv:
status = dv.get("status")
if status == "Unsupported":
cells[key]["unsupported"] += 1
elif status == "PartiallyConfirmed":
cells[key]["partially_confirmed"] += 1
elif status == "Confirmed":
cells[key]["confirmed"] += 1
# Repro-stability and false-Confirmed counts are optional
# fields tabulate.py reads off the verdict when callers have
# stamped them.
if dv.get("wrong") is True:
cells[key]["wrong_confirmed"] += 1
if dv.get("replay_stable") is True:
cells[key]["stable_replays"] += 1
if not args.inhouse and args.ground_truth and Path(args.ground_truth).exists():
gt = load_json(args.ground_truth)
# Ground truth format: list of {"path": ..., "line": ..., "cap": ..., "vuln": bool}
gt_true: list[dict] = []
for entry in gt if isinstance(gt, list) else []:
# Honour the same language scope as the findings filter so recall
# is measured only over the corpus's target language.
if lang_filter and lang_of(entry) not in lang_filter:
continue
if entry.get("vuln"):
gt_true.append({
"path": entry.get("path", ""),
"line": entry.get("line", 0),
"cap": entry.get("cap", ""),
})
# Track which GT entries were matched (by index) to avoid double-counting.
matched_gt: set[int] = set()
# Track (path, cap) pairs that had at least one finding match.
found_path_caps: set[tuple[str, str]] = set()
for f in findings:
f_path = f.get("path", "")
f_line = f.get("line", 0)
f_cap = cap_of(f, static_lens=args.static)
cap = f_cap
lang = lang_of(f)
cell_key = (cap, lang)
dv = (f.get("evidence") or {}).get("dynamic_verdict") or {}
is_confirmed = dv.get("status") == "Confirmed"
matched_idx = None
for idx, gt_entry in enumerate(gt_true):
if (path_matches(gt_entry["path"], f_path)
and gt_entry["cap"] == f_cap
and idx not in matched_gt
and (gt_entry["line"] == 0
or abs(gt_entry["line"] - f_line) <= LINE_TOLERANCE)):
matched_idx = idx
break
if matched_idx is not None:
matched_gt.add(matched_idx)
found_path_caps.add((f_path, f_cap))
cells[cell_key]["tp"] += 1
if is_confirmed:
cells[cell_key]["confirmed_tp"] += 1
else:
cells[cell_key]["fp"] += 1
if is_confirmed:
cells[cell_key]["confirmed_fp"] += 1
for idx, gt_entry in enumerate(gt_true):
if idx not in matched_gt:
cap = gt_entry["cap"]
# Land the FN in the cell its source language implies (from the
# GT path extension) so per-(cap,lang) recall is meaningful and
# OWASP misses show up in the java cell, not a stray "unknown".
cells[(cap, lang_of(gt_entry))]["fn"] += 1
# Ground-truth-derived false-confirm accounting. When a corpus ships a
# vuln/benign label per file (OWASP, SARD), a Confirmed finding that
# matches no GT positive is a false confirm — authoritative, so it
# overrides any manual-triage stamping for these labelled sets. This is
# what makes the per-cell `false_confirmed_rate` budget non-vacuous on a
# fresh eval corpus without a host-local verify-feedback log.
for v in cells.values():
if v["confirmed_tp"] or v["confirmed_fp"]:
v["wrong_confirmed"] = v["confirmed_fp"]
result = {
"label": args.label,
"total_findings": len(findings),
"cells": [
{
"cap": k[0],
"lang": k[1],
**v,
"precision": v["tp"] / max(v["tp"] + v["fp"], 1),
"recall": v["tp"] / max(v["tp"] + v["fn"], 1),
"unsupported_rate": v["unsupported"] / max(v["total"], 1),
}
for k, v in sorted(cells.items())
],
}
existing = load_json(args.append) if Path(args.append).exists() else []
existing.append(result)
with open(args.append, "w") as f:
json.dump(existing, f, indent=2)
# Print summary
print(f"\n=== {args.label} ===")
print(f"{'Cap':<20} {'Lang':<12} {'TP':>5} {'FP':>5} {'FN':>5} {'Prec':>6} {'Rec':>6} {'Unsup%':>7}")
print("-" * 72)
for c in result["cells"]:
print(
f"{c['cap']:<20} {c['lang']:<12} "
f"{c['tp']:>5} {c['fp']:>5} {c['fn']:>5} "
f"{c['precision']:>6.2f} {c['recall']:>6.2f} "
f"{c['unsupported_rate']*100:>6.1f}%"
)
exit_rc = 0
# ── Phase 29: per-cell budget enforcement ─────────────────────────────
if args.budget:
budget = load_budget(args.budget)
failures = enforce_budget(result["cells"], budget)
if failures:
print(f"\n=== Per-cell budget regressions ({args.budget}) ===")
for line in failures:
print(line)
exit_rc = 2
else:
print(f"\nPer-cell budget ({args.budget}): OK")
# ── Phase 29: diff against previous run ───────────────────────────────
if args.diff:
prev = load_previous_cells(args.diff, args.label)
failures = diff_regressions(result["cells"], prev)
if failures:
print(f"\n=== Monotonic-improvement regressions vs {args.diff} ===")
for line in failures:
print(line)
exit_rc = 2
else:
print(f"\nDiff vs {args.diff}: no regressions")
return exit_rc
if __name__ == "__main__":
sys.exit(main())
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