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How RNG and winning mechanics are tested
The honesty of the slot rests on two supports: high-quality RNG (random number generator) and the correct winning mechanics, which maps random numbers into outcomes without bias. Testing is not one luck test, but a whole system: RNG cryptographic strength, statistical checks, RTP/volatility monte carlo simulations, deterministic sides for reproducibility, audit logs and certification in independent laboratories. Below is a complete, practical conveyor.
1) RNG architecture: what makes up "randomness"
Sources of entropy: OS (CSPRNG/'/dev/urandom ', CryptGenRandom), hardware TRNG (where available), system noise.
Algorithm: server CSPRNG (for example, CTR_DRBG/HMAC_DRBG) or high-quality PRNG (PCG/Xoshiro) with flow independence control.
Seed policy: primary seed from CSPRNG, individual streams per session/game/feature, protection against reuse, safe storage (HSM/secure storage).
Server → client: the outcome is calculated on the server, the client is only visualization; any "preludes" (near-miss/teasers) do not affect the result.
Independence of spins: no auto-adjustment to balance; lack of "good luck strips."
Control question: at what stage is the result accepted? Answer: before playing the animation, with fixation in an unchangeable log.
2) RNG mapping → outcome (no bias)
The correct unfolding of random numbers in the weights of characters/cells is the key to the absence of "modular" and other shifts.
Uniform samples - If a number in the range '[0, N)' is required, use rejection sampling instead of 'rand ()% N' to exclude bias at '2 ^ k% N ≠ 0'.
Weighted samples: cumulative distributions (CDF) or Alias algorithm (Vose) for fast samples by weight.
Multiple pull: a separate RNG call for each reel/cell/event, and not "scattering" one number for the entire field.
Guarantees at the code level: property-based tests for invariants ("sum of frequencies ≈ weights," "no segment is underrepresented").
3) What exactly we check: goals and metrics
RTP (Return to Player) - average return,%- Volatility/variance - variance of results
- Hit Rate - frequency of any win
- Bonus Frequency
- Max Exposure - theoretical maximum (x of bid)
- Stationarity - no drift of distributions in time/releases
4) RNG statistical tests (off-line batteries)
Use "batteries" on long sequences (10⁸+ bits/values), separately for each RNG stream:- Moments and correlations: monobit test (proportion 0/1), autocorrelation (lag k), serial and paired correlations.
- Тесты NIST SP 800-22: frequency, block frequency, runs, longest run, FFT, approximate entropy.
- TestU01/Dieharder: additional "stress tests" (birthday spacings, matrix rank, random excursions).
- KS/ χ bucket ²: comparison of empirical and theoretical uniformity on '[0,1)' and on target ranges.
- Poker tests (for groups of bits) and "gap tests."
Acceptance criteria: p-values in the acceptable range (not "too ideal"), the absence of systematic failures on fixed side values, stable results on different platforms/compilers.
5) Mapping statistics (game-specific)
Even the perfect RNG can be ruined by the wrong mapping. We check the distribution of outcomes:- Frequencies of symbols/cells: χ ² for coincidence with weights (by reels/clusters/coins).
- Combinations/lines: binomial intervals for winning combinations; comparison with reference tables.
- Bonus triggers/retriggers: event intervals (geometric/negative binomial) + KS/AD tests.
- Independence of drums: cross-correlations between positions (exclude "sticking").
6) Monte Carlo simulations of RTP/volatility/frequencies
Reproducible simulations are the core of QA mathematics.
1. Setting: fix the version of mathematics, sids, weights/thongs/paytable.
2. Run: ≥10⁷ - spin 10⁸ for tail stability; separately - long bonus sessions.
3. Estimates and intervals:- RTP score: (\hat {RTP} =\bar {X}), where (X) is the gain in xBet.
- Confidence interval (CLT): (\hat {RTP }\pm z_{\alpha/2}\cdot s/\sqrt {n}).
- Required sample: (n\approach (z\cdot s/\varepsilon) ^ 2) for error (\varepsilon).
- For Hit Rate/Bonus Rate, binomial (Wilson) intervals.
- 4. Tails: p95/p99/p99. 9 wins per spin and per bonus; control "max exposure."
- 5. Stability: sensitivity to ± δ changes in weights ("robustness runs").
7) Determinacy and reproducibility
Deterministic sides for QA: same sid → same outcomes (golden-run).
Identical results on platforms: compiler/library version fix, endianness check, FPU modes.
Save states: restore the interrupted bonus/spin without "flipping" the result.
Replay infrastructure: launching a "problematic" seed + step ticket for analysis.
8) Security and anti-tamper
WORM logs (or merkly hash chains): recording the outcome and input parameters before animation.
Signatures of builds and math lists: version of pay tables/scales - in the manifest with a signature.
Client integrity control: obfuscation, hash checking, anti-instrumentation.
Server-authoritative: only the server decides the outcome; client does not contain "hidden" checks.
9) Load and long-term tests
Soak tests: hundreds of millions of spin with rotation of sides; monitoring of memory/resource leaks.
High competition: parallel sessions of RNG streams → no racing/lock contention.
Network degrades: repeated requests/timeouts do not change the spin result.
10) Validation of UX invariants (interface integrity)
Near-miss: Animations don't change probability; banning "rigging" stops for the sake of drama.
Spin speed: Acceleration/turbo does not affect RNG.
Instructional/demo modes: either honest or tagged and math separated.
11) Post-release monitoring (statistical control in sales)
SPC cards/control graphs: RTP by time windows/casino/geo - in acceptable corridors.
Drift detection: PSI/JS divergence of win/frequency distributions.
Alarms: deviations → game/market blocking, log recalculation, report.
12) Certification and documentation
Prepare lab package (GLI/eCOGRA/BMM/iTech, etc.):- RNG description: algorithm, sources of entropy, crop policy, independence of flows.
- Sources/binaries of the RNG module (or inspection artifacts) + test logs.
- Math Sheet: payout tables, weights, RTP breakdown (base/bonus/jackpot), max exposure.
- Simulation reports: volume, metrics, confidence intervals.
- Logs/replays: format, signatures, retention policy.
- Versioning: unchangeable hashes of artifacts (build, assets, math).
13) Frequent mistakes and how to avoid them
'rand ()% N'and modular offset. Use rejection/alias.
One RNG for everything without threads. Do independent streams, avoid hidden correlations.
Mapping "by beautiful indexes." Always check frequencies against weights χ ² tests.
Small simulations. 10⁶ is a "smoke check," tails need 10⁸.
Lack of deterministic sides. Without them, you cannot reproduce bugs.
The client decides the outcome. Server only, WORM logs only.
No post-monitoring. The release is not the end, but the beginning of statistical control.
14) Formulas and mini cheat sheet
χ uniformity ² (k buckets):[
\chi^2=\sum_{i=1}^k \frac{(O_i-E_i)^2}{E_i},\quad E_i=n/k
]Compare with (\chi ^ 2 _ {k-1}).
KS for continuous distribution:[
D=\sup_x F_n(x)-F(x)
][
\hat{\mu}\pm z_{\alpha/2}\frac{s}{\sqrt{n}}
][
\frac{p+\frac{z^2}{2n}\pm z\sqrt{\frac{p(1-p)}{n}+\frac{z^2}{4n^2}}}{1+\frac{z^2}{n}}
]15) Checklists
Technical design RNG
- CSPRNG/TRNG source; documented seed/stream policy
- Independent streams, no shared-state racing
- Rejection/alias instead of '%'
- Server-authoritative; result fix before animation
- WORM logs, artifact signatures
Statistics and simulations
- Batterey NIST/TestU01/Dieharder - Passed
- χ ²/KS/wound - on outcome mapping
- ≥10⁷ - spin 10⁸; CI by RTP/frequencies in tolerances
- p95/p99/p99 tails. 9 and max exposure under control
- Robustness runs when ± δ to scales
QA/Engineering
- Deterministic sides; replay tickets
- Soak/load; memory/CPU/latency stability
- Spin/bonus summary without change of outcome
- Cross-platform identity of results
Compliance/Documents
- RNG specification + source/artifacts
- Math Sheet + simulation reports
- Logging/Retention/Audit Policies
- Versioning and build/paytable hashes
RNG and winning mechanics testing is statistics and safety engineering. You protect players and brand when:
1. RNG stands and correctly seeded, 2. mapping outcomes without bias and reproducible, 3. RTP/frequencies/tails confirmed by large simulations, 4. the outcome is captured and audited before animation, 5. post-release monitoring catches any drift.
So the slot remains honest, predictable (in a statistical sense) and resistant to manipulation - and you pass certification and build long-term trust.