%% SCRAP: experiments/campaigns/doe_2x7/README
%% SOURCE: docs/working/experiments/campaigns/doe_2x7/README.md
%% STATUS: CURRENT
%% FITS: experiments/ch-factorial
%% EDITORIAL: lifted — prose rewritten to press voice
\section{$2^7$ Factorial Design of Experiments}
The $2^7$ DoE campaign exhaustively tested all 128 combinations of
StarForth's seven adaptive feedback loops (L1--L7) to identify optimal
configurations and interaction effects. It represents the empirical
foundation for the L8 Jacquard mode selector.
\textbf{Scale:} 128 configurations $\times$ 300 replicates = 38\,400
total runs. Duration approximately 6--8 hours on modern AMD64 hardware.
Reference commit: \texttt{161a3667}.
\subsection{Experimental Design}
\subsubsection{Independent Variables}
\begin{center}
\begin{tabular}{llll}
\toprule
Loop & Factor & Description & Hypothesis \\
\midrule
L1 & \texttt{HEAT\_TRACKING} & Execution frequency tracking & May cause cache thrashing \\
L2 & \texttt{ROLLING\_WINDOW} & Execution history buffer & Enables pattern detection \\
L3 & \texttt{LINEAR\_DECAY} & Heat dissipation over time & Prevents stale accumulation \\
L4 & \texttt{PIPELINING\_METRICS} & Word transition prediction & May add overhead \\
L5 & \texttt{WINDOW\_INFERENCE} & Adaptive window sizing (Levene's) & Optimizes L2 window \\
L6 & \texttt{DECAY\_INFERENCE} & Exponential regression on heat & Optimizes L3 decay slope \\
L7 & \texttt{ADAPTIVE\_HEARTRATE} & Dynamic tick frequency & Reduces overhead when stable \\
\bottomrule
\end{tabular}
\end{center}
\subsubsection{Configuration Encoding}
Each configuration is a 7-bit binary number with bit position $n$
controlling loop $L(n+1)$. Configuration C97 (binary \texttt{1100001})
enables L1, L5, and L6.
\subsubsection{Dependent Variables}
Primary: \texttt{ns\_per\_word} (execution time per FORTH word) and
\texttt{cv} (coefficient of variation as stability metric).
Secondary: \texttt{window\_width}, \texttt{decay\_slope\_q48},
\texttt{total\_heat}, \texttt{hot\_word\_count}, \texttt{prefetch\_hits}.
\subsection{Key Findings (300 Replicates, November 2025)}
\subsubsection{Loop Effectiveness in Top 5\% Configurations}
\begin{center}
\begin{tabular}{llll}
\toprule
Loop & Effect & Top-5\% Prevalence & Recommendation \\
\midrule
L1 (Heat) & Harmful & 14\% enabled & Disable by default \\
L2 (Window) & Workload-dependent & 57\% enabled & L8-controlled \\
L3 (Decay) & Beneficial & 57\% enabled & L8-controlled \\
L4 (Pipeline) & Harmful & 0\% enabled & Disable by default \\
L5 (Window Inf.) & Beneficial & 43\% enabled & L8-controlled \\
L6 (Decay Inf.) & Workload-dependent & 57\% enabled & L8-controlled \\
L7 (Heartrate) & Beneficial & 71\% enabled & Always on \\
\bottomrule
\end{tabular}
\end{center}
\subsubsection{Top Configurations}
\begin{center}
\begin{tabular}{lllll}
\toprule
Rank & Config & Binary & Loops & Character \\
\midrule
1 & C97 & \texttt{0110001} & L1+L5+L6 & Temporal+diverse \\
2 & C7 & \texttt{0000111} & L3+L5+L6 & Full inference \\
3 & C75 & \texttt{0100011} & L2+L6 & Diverse+decay \\
4 & C70 & \texttt{0100110} & L2+L5+L6 & Diverse+inference \\
5 & C1 & \texttt{0000001} & L1 only & Minimal \\
\bottomrule
\end{tabular}
\end{center}
\textbf{Critical insight:} No single configuration is optimal across all
workload types. This finding directly motivates the L8 Jacquard dynamic
mode selector.
\subsection{Running the Experiment}
\begin{lstlisting}[language=bash]
# Generate 128-configuration run matrix
cd experiments/doe_2x7
./generate_run_matrix.sh
# Run full DoE (300 replicates, ~6-8 hours)
./run_doe.sh 300
# Quick test (10 replicates, ~15 minutes)
./run_doe.sh 10
\end{lstlisting}
Results land in a timestamped directory containing raw CSV data, summary
statistics, ANOVA interaction results, and 56 visualization plots
(per-loop distributions, pairwise interaction plots, performance heatmap,
Pareto frontier).
\subsection{Analysis}
The auto-generated R analysis script (\texttt{doe\_full\_report.R})
produces ANOVA tables with main effects and interactions, Cohen's $d$ and
$\eta^2$ effect sizes, and Pareto frontiers of speed versus stability.
Requires R $\geq 4.0$ with \texttt{ggplot2}, \texttt{dplyr},
\texttt{tidyr}, and \texttt{gridExtra}.
%% PATENT: the loop effectiveness findings and their relationship to
%% the L8 selector mechanism are patent-adjacent. Do not draft claims here.
