Add instruction count/fault count correlation plot

scripts/charts/combined_instr_fault_correlation.r

@@ -0,0 +1,132 @@
+library(ggplot2)
+library(dplyr)
+library(readr)
+
+# Usage: Rscript combined_instr_fault_correlation.r exp_abspath1 exp_abspath2 ... [faults_file]
+
+args <- commandArgs(trailingOnly = TRUE)
+if (length(args) < 1) {
+  stop("Need at least 1 experiment")
+}
+
+# TODO: I should probably stop duplicating this each time
+csv_suffix <- if (grepl("\\.csv$", args[length(args)])) {
+  args[length(args)]
+} else {
+  "faults.csv"
+}
+exp_args <- if (grepl("\\.csv$", args[length(args)])) {
+  args[-length(args)]
+} else {
+  args
+}
+
+# Don't use counts from faults.csv, as that would correlate completely
+# because only mnemonics with faults are listed there
+# NOTE: Doesn't match selected filters for faults.csv
+mnemonics_file <- "mnemonics.csv"
+
+freq_data <- data.frame()
+faults_data <- data.frame()
+
+# TODO: I should probably stop duplicating this each time
+for (arg in exp_args) {
+  mnem_path <- file.path(arg, mnemonics_file)
+  faults_path <- file.path(arg, csv_suffix)
+
+  if (!file.exists(mnem_path)) {
+    warning(paste("Missing:", mnem_path))
+    next
+  }
+  if (!file.exists(faults_path)) {
+    warning(paste("Missing:", faults_path))
+    next
+  }
+
+  mdf <- read_csv(mnem_path, col_types = cols())
+  mdf$experiment <- basename(arg)
+  freq_data <- bind_rows(freq_data, mdf)
+
+  fdf <- read_csv(faults_path, col_types = cols())
+  fdf$experiment <- basename(arg)
+  faults_data <- bind_rows(faults_data, fdf)
+}
+
+if (nrow(freq_data) == 0 || nrow(faults_data) == 0) {
+  stop("No data loaded")
+}
+
+# x-axis: mnemonics.csv counts
+instr_freq <- freq_data |>
+  filter(!is.na(mnemonic), mnemonic != "NULL") |>
+  group_by(mnemonic) |>
+  summarise(instr_count = sum(count, na.rm = TRUE), .groups = "drop")
+
+# y-axis: no OK_MARKERs, sum GROUP1_MARKER + TRAP.
+marker_count <- faults_data |>
+  filter(!is.na(mnemonic), mnemonic != "NULL") |>
+  filter(resulttype != "OK_MARKER") |>
+  mutate(
+    resulttype = ifelse(resulttype == "GROUP1_MARKER", "TRAP", resulttype)
+  ) |>
+  group_by(mnemonic) |>
+  summarise(marker_count = sum(faults, na.rm = TRUE), .groups = "drop")
+
+correlation <- instr_freq |>
+  inner_join(marker_count, by = "mnemonic") |>
+  filter(instr_count > 0, marker_count > 0)
+
+if (nrow(correlation) < 2) {
+  stop("Not enough mnemonics to compute a correlation")
+}
+
+cor_raw <- cor(
+  correlation$instr_count,
+  correlation$marker_count,
+  method = "pearson"
+)
+cor_log <- cor(
+  log10(correlation$instr_count),
+  log10(correlation$marker_count),
+  method = "pearson"
+)
+cat(sprintf("Pearson correlation (raw):     %.4f\n", cor_raw))
+cat(sprintf("Pearson correlation (log10):   %.4f\n", cor_log))
+
+plot <- ggplot(
+  correlation,
+  aes(x = instr_count, y = marker_count)
+) +
+  geom_smooth(
+    method = "lm",
+    se = FALSE,
+    colour = "grey50",
+    linetype = "dashed"
+  ) +
+  geom_point(aes(colour = mnemonic), size = 3, alpha = 0.8) +
+  geom_text(
+    aes(label = mnemonic),
+    size = 3,
+    vjust = -0.8,
+    check_overlap = TRUE
+  ) +
+  scale_x_log10(name = "Instruction Execution Frequency") +
+  scale_y_log10(name = "Failure-Marker Occurrences") +
+  labs(
+    title = sprintf(
+      "Instruction / Fault Correlation (r_raw = %.4f, r_log = %.4f)",
+      cor_raw,
+      cor_log
+    ),
+    colour = "Mnemonic"
+  ) +
+  theme_minimal() +
+  theme(
+    legend.position = "none",
+    plot.title = element_text(size = 14, face = "bold")
+  )
+
+suffix <- gsub("^faults|\\.csv$", "", csv_suffix)
+outfile <- paste0("injections/instr_fault_correlation", suffix, ".svg")
+ggsave(outfile, plot = plot, width = 10, height = 8)
+print(paste("Saved", outfile))

targets/lib.h

@@ -39,7 +39,7 @@
 #endif
 
 #ifdef TARGET_LINUX
-#include "stdio.h"
+#include <stdio.h>
 #include <string.h>
 #define MAIN int main(int argc, char *argv[])
 #define PRINT(fmt, ...) fprintf(stdout, fmt, ##__VA_ARGS__)