Group fault rates / instruction plot per experiment

scripts/charts/combined_fault_count_comparison.r

@@ -1,8 +1,11 @@
 library(ggplot2)
 library(dplyr)
 library(readr)
+library(stringr)
 
 # Usage: Rscript combined_comparison.r exp_abspath1 exp_abspath2 ... [resultsdata_file]
+# One coordinate system per base experiment (facet); c/aot/interp variants
+# share each facet, coloured by variant.
 
 args <- commandArgs(trailingOnly = TRUE)
 if (length(args) < 1) {
@@ -20,17 +23,36 @@ exp_args <- if (grepl("\\.csv$", args[length(args)])) {
   args
 }
 
+extract_info <- function(path) {
+  dir_name <- basename(path)
+  match <- str_match(
+    dir_name,
+    "^\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}_(.+?)_(c|aot|interp)_"
+  )
+  if (is.na(match[1, 1])) {
+    warning(paste("Could not parse:", dir_name))
+    return(NULL)
+  }
+  list(base_name = match[1, 2], variant = match[1, 3], path = path)
+}
+
 all_data <- data.frame()
 
 for (arg in exp_args) {
-  csv_file <- file.path(arg, csv_suffix)
+  info <- extract_info(arg)
+  if (is.null(info)) {
+    next
+  }
+
+  csv_file <- file.path(info$path, csv_suffix)
   if (!file.exists(csv_file)) {
     warning(paste("Missing:", csv_file))
     next
   }
 
   df <- read_csv(csv_file, col_types = cols())
-  df$experiment <- basename(arg)
+  df$base_name <- info$base_name
+  df$variant <- info$variant
   all_data <- bind_rows(all_data, df)
 }
 
@@ -39,7 +61,7 @@ if (nrow(all_data) == 0) {
 }
 
 totals <- all_data |>
-  group_by(experiment, resulttype) |>
+  group_by(base_name, variant, resulttype) |>
   summarise(faults = sum(faults, na.rm = TRUE), .groups = "drop") |>
   ungroup()
 
@@ -57,21 +79,25 @@ totals$resulttype <- factor(totals$resulttype, levels = marker_order)
 
 plot <- ggplot(
   totals,
-  aes(x = resulttype, y = faults, colour = experiment, group = experiment)
+  aes(x = resulttype, y = faults, colour = variant, group = variant)
 ) +
   geom_point(size = 2) +
   geom_line() +
+  facet_wrap(~base_name) +
   scale_y_log10() +
   labs(
-    x = "Marker",
-    y = "Faults",
-    title = "Combined Comparison",
-    color = "Experiment"
+    x = "Fault Type",
+    y = "Fault Count",
+    title = "Fault Count Comparison",
+    color = "Variant"
   ) +
   theme_minimal() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+  theme(
+    axis.text.x = element_text(angle = 90, hjust = 1),
+    plot.title = element_text(size = 14, face = "bold")
+  )
 
 suffix <- gsub("^resultsdata|\\.csv$", "", csv_suffix)
-outfile <- paste0("injections/combined_comparison", suffix, ".svg")
+outfile <- paste0("injections/fault_count_comparison", suffix, ".svg")
 ggsave(outfile, plot = plot, width = 12, height = 6)
 print(paste("Saved", outfile))

scripts/charts/combined_fault_count_correlation.r

@@ -4,8 +4,8 @@ library(readr)
 library(stringr)
 library(tidyr)
 
-# Usage: Rscript ratio_correlation.r exp_abspath1 exp_abspath2 ... [resultsdata_file]
-# Plots correlation between aot/c and interp/c ratios
+# Usage: Rscript combined_fault_correlation.r exp_abspath1 exp_abspath2 ... [resultsdata_file]
+# Plots correlation between raw aot and interp fault counts (no C baseline).
 
 args <- commandArgs(trailingOnly = TRUE)
 if (length(args) < 2) {
@@ -69,48 +69,52 @@ all_data <- all_data |>
   group_by(base_name, variant, benchmark, resulttype) |>
   summarise(faults = sum(faults), .groups = "drop")
 
-baseline <- all_data |> filter(variant == "c")
-comparisons <- all_data |> filter(variant != "c")
+# Only aot/interp matter; C is not used as a baseline here.
+counts <- all_data |> filter(variant %in% c("aot", "interp"))
 
-ratios <- comparisons |>
-  left_join(
-    baseline |> select(base_name, benchmark, resulttype, faults),
-    by = c("base_name", "benchmark", "resulttype"),
-    suffix = c("", "_baseline")
-  ) |>
-  filter(!is.na(faults_baseline), faults_baseline > 0) |>
-  mutate(ratio = faults / faults_baseline)
-
-if (nrow(ratios) == 0) {
-  stop("No ratios computed (missing baseline or zero values)")
-}
-
-# Pivot to get aot and interp ratios side by side
-ratio_wide <- ratios |>
-  select(base_name, benchmark, resulttype, variant, ratio) |>
-  pivot_wider(names_from = variant, values_from = ratio) |>
+# Pivot to get aot and interp fault counts side by side
+counts_wide <- counts |>
+  select(base_name, benchmark, resulttype, variant, faults) |>
+  pivot_wider(names_from = variant, values_from = faults) |>
   filter(!is.na(aot), !is.na(interp))
 
-if (nrow(ratio_wide) == 0) {
-  stop("No paired aot/interp ratios found")
+if (nrow(counts_wide) == 0) {
+  stop("No paired aot/interp fault counts found")
 }
 
 # Compute correlation
-cor_result <- cor(ratio_wide$aot, ratio_wide$interp, method = "pearson")
-cat(sprintf("Pearson correlation: %.4f\n", cor_result))
+cor_raw <- cor(counts_wide$aot, counts_wide$interp, method = "pearson")
+cor_log <- cor(
+  log10(counts_wide$aot),
+  log10(counts_wide$interp),
+  method = "pearson"
+)
+cat(sprintf("Pearson correlation (raw):   %.4f\n", cor_raw))
+cat(sprintf("Pearson correlation (log10): %.4f\n", cor_log))
 
 # Create plot
 plot <- ggplot(
-  ratio_wide,
+  counts_wide,
   aes(x = aot, y = interp, color = base_name, shape = resulttype)
 ) +
+  # geom_abline(
+  #   slope = 1,
+  #   intercept = 0,
+  #   colour = "grey70",
+  #   linetype = "dotted"
+  # ) +
   geom_point(size = 3, alpha = 0.7) +
-  scale_x_log10(name = "AOT / C Ratio") +
-  scale_y_log10(name = "Interpreter / C Ratio") +
+  scale_x_log10(name = "AOT Fault Count") +
+  scale_y_log10(name = "Interpreter Fault Count") +
   labs(
-    title = sprintf("Ratio Correlation (r = %.4f)", cor_result),
+    # title = sprintf(
+    #   "Fault Count Correlation (r_raw = %.4f, r_log = %.4f)",
+    #   cor_raw,
+    #   cor_log
+    # ),
+    title = "Fault Count Correlation",
     color = "Experiment",
-    shape = "Marker"
+    shape = "Fault Type"
   ) +
   theme_minimal() +
   theme(
@@ -118,5 +122,7 @@ plot <- ggplot(
     plot.title = element_text(size = 14, face = "bold")
   )
 
-ggsave("injections/ratio_correlation.svg", plot = plot, width = 10, height = 8)
-print("Saved ratio_correlation.svg")
+suffix <- gsub("^resultsdata|\\.csv$", "", csv_suffix)
+outfile <- paste0("injections/fault_count_correlation", suffix, ".svg")
+ggsave(outfile, plot = plot, width = 10, height = 8)
+print(paste("Saved", outfile))

scripts/charts/combined_fault_rates_per_instruction.r

@@ -0,0 +1,115 @@
+library(ggplot2)
+library(dplyr)
+library(readr)
+library(stringr)
+
+# Usage: Rscript combined_fault_rates.r exp_abspath1 exp_abspath2 ... [faults_file]
+
+args <- commandArgs(trailingOnly = TRUE)
+if (length(args) < 1) {
+  stop("Need at least 1 experiment")
+}
+
+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
+}
+
+# Faults / instruction count, per experiment
+rates <- data.frame()
+
+for (arg in exp_args) {
+  faults_file <- file.path(arg, csv_suffix)
+  mnem_file <- file.path(arg, "mnemonics.csv")
+
+  if (!file.exists(faults_file)) {
+    warning(paste("Missing:", faults_file))
+    next
+  }
+  if (!file.exists(mnem_file)) {
+    warning(paste("Missing:", mnem_file))
+    next
+  }
+
+  df <- read_csv(faults_file, col_types = cols())
+  mdf <- read_csv(mnem_file, col_types = cols())
+
+  total_faults <- df |>
+    filter(resulttype != "OK_MARKER") |>
+    summarise(faults = sum(faults, na.rm = TRUE)) |>
+    pull(faults)
+
+  total_instrs <- sum(mdf$count, na.rm = TRUE)
+
+  if (is.na(total_instrs) || total_instrs == 0) {
+    warning(paste("Zero instruction count for", arg))
+    next
+  }
+
+  match <- str_match(
+    basename(arg),
+    "^\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}_(.+?)_(c|aot|interp)_"
+  )
+  if (is.na(match[1, 1])) {
+    base_name <- basename(arg)
+    variant <- "unknown"
+  } else {
+    base_name <- match[1, 2]
+    variant <- match[1, 3]
+  }
+
+  rates <- bind_rows(
+    rates,
+    data.frame(
+      base_name = base_name,
+      variant = variant,
+      fault_rate = total_faults / total_instrs
+    )
+  )
+}
+
+if (nrow(rates) == 0) {
+  stop("No data loaded")
+}
+
+# Order base_names by their max fault rate
+base_order <- rates |>
+  group_by(base_name) |>
+  summarise(max_rate = max(fault_rate), .groups = "drop") |>
+  arrange(desc(max_rate)) |>
+  pull(base_name)
+
+rates <- rates |>
+  mutate(
+    base_name = factor(base_name, levels = base_order),
+    variant = factor(variant, levels = c("c", "aot", "interp", "unknown"))
+  )
+
+plot <- ggplot(
+  rates,
+  aes(x = base_name, y = fault_rate, fill = variant)
+) +
+  geom_col(position = position_dodge(preserve = "single")) +
+  scale_y_log10() +
+  labs(
+    title = "Fault Rate per Instruction",
+    x = "Experiment",
+    y = "Faults / Instruction Count",
+    fill = "Variant"
+  ) +
+  theme_minimal() +
+  theme(
+    axis.text.x = element_text(angle = 90, hjust = 1),
+    plot.title = element_text(size = 14, face = "bold")
+  )
+
+suffix <- gsub("^faults|\\.csv$", "", csv_suffix)
+outfile <- paste0("injections/fault_rates_per_instruction", suffix, ".svg")
+ggsave(outfile, plot = plot, width = 12, height = 6)
+print(paste("Saved", outfile))

scripts/charts/combined_instr_fault_correlation.r

@@ -0,0 +1,133 @@
+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 Executions") +
+  scale_y_log10(name = "Fault Count") +
+  labs(
+    # title = sprintf(
+    #   "Instruction / Fault Correlation (r_raw = %.4f, r_log = %.4f)",
+    #   cor_raw,
+    #   cor_log
+    # ),
+    title = "Instruction / Fault Correlation",
+    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))

scripts/charts/combined_instr_fault_rate_heatmap.r

@@ -0,0 +1,120 @@
+library(ggplot2)
+library(dplyr)
+library(readr)
+library(viridisLite)
+
+# Usage: Rscript combined_instr_fault_correlation_heatmap.r exp_abspath1 ... [faults_file]
+
+args <- commandArgs(trailingOnly = TRUE)
+if (length(args) < 1) {
+  stop("Need at least 1 experiment")
+}
+
+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
+}
+
+all_data <- data.frame()
+all_mnem <- data.frame()
+
+for (arg in exp_args) {
+  csv_file <- file.path(arg, csv_suffix)
+  if (!file.exists(csv_file)) {
+    warning(paste("Missing:", csv_file))
+    next
+  }
+
+  df <- read_csv(csv_file, col_types = cols())
+  df$experiment <- basename(arg)
+  all_data <- bind_rows(all_data, df)
+
+  # TODO: This is ignoring any filters currently
+  mnem_file <- file.path(arg, "mnemonics.csv")
+  if (!file.exists(mnem_file)) {
+    warning(paste("Missing:", mnem_file))
+    next
+  }
+  mdf <- read_csv(mnem_file, col_types = cols())
+  all_mnem <- bind_rows(all_mnem, mdf)
+}
+
+if (nrow(all_data) == 0) {
+  stop("No faults.csv data loaded")
+}
+
+if (nrow(all_mnem) == 0) {
+  stop("No mnemonics.csv data loaded")
+}
+
+# no OK_MARKER, sum GROUP1 + TRAP.
+all_data <- all_data |>
+  filter(!is.na(mnemonic), mnemonic != "NULL") |>
+  filter(resulttype != "OK_MARKER") |>
+  mutate(
+    resulttype = ifelse(resulttype == "GROUP1_MARKER", "TRAP", resulttype)
+  )
+
+if (nrow(all_data) == 0) {
+  stop("No failure-marker data to plot")
+}
+
+# Sum faults per (mnemonic, marker) pair for all experiments
+heat <- all_data |>
+  group_by(mnemonic, resulttype) |>
+  summarise(faults = sum(faults, na.rm = TRUE), .groups = "drop")
+
+# Sum mnemonic counts for all experiments
+mnem_counts <- all_mnem |>
+  filter(!is.na(mnemonic), mnemonic != "NULL") |>
+  group_by(mnemonic) |>
+  summarise(count = sum(count, na.rm = TRUE), .groups = "drop")
+
+# Normalize by mnemonic count
+heat <- heat |>
+  left_join(mnem_counts, by = "mnemonic") |>
+  filter(!is.na(count), count > 0) |>
+  mutate(fault_rate = faults / count)
+
+if (nrow(heat) == 0) {
+  stop("Heat join failed")
+}
+
+# Order by fault rate
+mnem_order <- heat |>
+  group_by(mnemonic) |>
+  summarise(total = sum(fault_rate), .groups = "drop") |>
+  arrange(desc(total)) |>
+  pull(mnemonic)
+
+heat <- heat |>
+  mutate(mnemonic = factor(mnemonic, levels = mnem_order))
+
+plot <- ggplot(
+  heat,
+  aes(x = mnemonic, y = resulttype, fill = fault_rate)
+) +
+  geom_tile(colour = "white") +
+  scale_fill_viridis_c(name = "Fault rate", trans = "log10") +
+  labs(
+    title = "Instruction / Fault Rate Heatmap (Normalized)",
+    x = "Instruction",
+    y = "Fault Type"
+  ) +
+  theme_minimal() +
+  theme(
+    axis.text.x = element_text(angle = 90, hjust = 1),
+    panel.grid = element_blank(),
+    plot.title = element_text(size = 14, face = "bold")
+  )
+
+suffix <- gsub("^faults|\\.csv$", "", csv_suffix)
+outfile <- paste0("injections/instr_fault_rate_heatmap", suffix, ".svg")
+ggsave(outfile, plot = plot, width = 12, height = 6)
+print(paste("Saved", outfile))

scripts/charts/combined_ratio_comparison.r

@@ -83,12 +83,18 @@ plot <- ggplot(
 ) +
   geom_point(size = 2) +
   geom_line() +
-  facet_wrap(~base_name, scales = "free_x") +
+  facet_wrap(~base_name) +
   scale_y_log10(name = "Ratio (to C)") +
-  scale_x_discrete(name = "Marker") +
-  labs(color = "Variant") +
+  scale_x_discrete(name = "Fault Type") +
+  labs(
+    title = "Fault Count Ratios",
+    color = "Variant"
+  ) +
   theme_minimal() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+  theme(
+    axis.text.x = element_text(angle = 90, hjust = 1),
+    plot.title = element_text(size = 14, face = "bold")
+  )
 
 ggsave("injections/ratio_comparison.svg", plot = plot, width = 12, height = 8)
 print("Saved ratio_comparison.svg")

scripts/charts/combined_ratio_comparison_merged.r

@@ -3,8 +3,8 @@ library(dplyr)
 library(readr)
 library(stringr)
 
-# Usage: Rscript ratio_comparison_merged.r exp_abspath1 exp_abspath2 ... [resultsdata_file]
-# Sums all benchmarks
+# Usage: Rscript ratio_comparison_merged_trap.r exp_abspath1 exp_abspath2 ... [resultsdata_file]
+# Sums all benchmarks, merges GROUP1_MARKER into TRAP
 
 args <- commandArgs(trailingOnly = TRUE)
 if (length(args) < 2) {
@@ -59,7 +59,9 @@ if (nrow(all_data) == 0) {
   stop("No data loaded")
 }
 
-# Add all benchs together (per marker type)
+all_data <- all_data |>
+  mutate(resulttype = ifelse(resulttype == "GROUP1_MARKER", "TRAP", resulttype))
+
 merged_data <- all_data |>
   group_by(base_name, variant, resulttype) |>
   summarise(faults = sum(faults), .groups = "drop")
@@ -88,15 +90,21 @@ plot <- ggplot(
   geom_line() +
   facet_wrap(~base_name) +
   scale_y_log10(name = "Ratio (to C)") +
-  scale_x_discrete(name = "Marker") +
-  labs(color = "Variant") +
+  scale_x_discrete(name = "Fault Type") +
+  labs(
+    color = "Variant",
+    title = "Fault Count Ratios"
+  ) +
   theme_minimal() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+  theme(
+    axis.text.x = element_text(angle = 90, hjust = 1),
+    plot.title = element_text(size = 14, face = "bold")
+  )
 
 ggsave(
-  "injections/ratio_comparison_merged.svg",
+  "injections/ratio_comparison_merged_trap.svg",
   plot = plot,
   width = 12,
   height = 8
 )
-print("Saved ratio_comparison_merged.svg")
+print("Saved ratio_comparison_merged_trap.svg")

scripts/charts/combined_ratio_comparison_merged_trap.r

@@ -1,100 +0,0 @@
-library(ggplot2)
-library(dplyr)
-library(readr)
-library(stringr)
-
-# Usage: Rscript ratio_comparison_merged_trap.r exp_abspath1 exp_abspath2 ... [resultsdata_file]
-# Sums all benchmarks, merges GROUP1_MARKER into TRAP
-
-args <- commandArgs(trailingOnly = TRUE)
-if (length(args) < 2) {
-  stop("Need at least 2 experiments")
-}
-
-csv_suffix <- if (grepl("\\.csv$", args[length(args)])) {
-  args[length(args)]
-} else {
-  "resultsdata.csv"
-}
-exp_args <- if (grepl("\\.csv$", args[length(args)])) args[-length(args)] else args
-
-extract_info <- function(path) {
-  dir_name <- basename(path)
-  match <- str_match(
-    dir_name,
-    "^\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}_(.+?)_(c|aot|interp)_"
-  )
-  if (is.na(match[1, 1])) {
-    warning(paste("Could not parse:", dir_name))
-    return(NULL)
-  }
-  list(base_name = match[1, 2], variant = match[1, 3], path = path)
-}
-
-all_data <- data.frame()
-
-for (arg in exp_args) {
-  info <- extract_info(arg)
-  if (is.null(info)) {
-    next
-  }
-
-  csv_file <- file.path(info$path, csv_suffix)
-  if (!file.exists(csv_file)) {
-    warning(paste("Missing:", csv_file))
-    next
-  }
-
-  df <- read_csv(csv_file, col_types = cols())
-  df$base_name <- info$base_name
-  df$variant <- info$variant
-  all_data <- bind_rows(all_data, df)
-}
-
-if (nrow(all_data) == 0) {
-  stop("No data loaded")
-}
-
-all_data <- all_data |>
-  mutate(resulttype = ifelse(resulttype == "GROUP1_MARKER", "TRAP", resulttype))
-
-merged_data <- all_data |>
-  group_by(base_name, variant, resulttype) |>
-  summarise(faults = sum(faults), .groups = "drop")
-
-baseline <- merged_data |> filter(variant == "c")
-comparisons <- merged_data |> filter(variant != "c")
-
-ratios <- comparisons |>
-  left_join(
-    baseline |> select(base_name, resulttype, faults),
-    by = c("base_name", "resulttype"),
-    suffix = c("", "_baseline")
-  ) |>
-  filter(!is.na(faults_baseline), faults_baseline > 0) |>
-  mutate(ratio = faults / faults_baseline)
-
-if (nrow(ratios) == 0) {
-  stop("No ratios computed (missing baseline or zero values)")
-}
-
-plot <- ggplot(
-  ratios,
-  aes(x = resulttype, y = ratio, color = variant, group = variant)
-) +
-  geom_point(size = 2) +
-  geom_line() +
-  facet_wrap(~base_name) +
-  scale_y_log10(name = "Ratio (to C)") +
-  scale_x_discrete(name = "Marker") +
-  labs(color = "Variant") +
-  theme_minimal() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1))
-
-ggsave(
-  "injections/ratio_comparison_merged_trap.svg",
-  plot = plot,
-  width = 12,
-  height = 8
-)
-print("Saved ratio_comparison_merged_trap.svg")

scripts/charts/combined_ratio_correlation_customized.r

@@ -1,129 +0,0 @@
-library(ggplot2)
-library(dplyr)
-library(readr)
-library(stringr)
-library(tidyr)
-
-# Usage: Rscript ratio_correlation_no_mem.r exp_abspath1 exp_abspath2 ... [resultsdata_file]
-# Plots correlation between aot/c and interp/c ratios, ignoring mem benchmark
-# NOTE: Just a copy of the ratio_correlation.r script where I've changed the filter in line 67
-
-args <- commandArgs(trailingOnly = TRUE)
-if (length(args) < 2) {
-  stop("Need at least 2 experiments")
-}
-
-csv_suffix <- if (grepl("\\.csv$", args[length(args)])) {
-  args[length(args)]
-} else {
-  "resultsdata.csv"
-}
-exp_args <- if (grepl("\\.csv$", args[length(args)])) {
-  args[-length(args)]
-} else {
-  args
-}
-
-extract_info <- function(path) {
-  dir_name <- basename(path)
-  match <- str_match(
-    dir_name,
-    "^\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}_(.+?)_(c|aot|interp)_"
-  )
-  if (is.na(match[1, 1])) {
-    warning(paste("Could not parse:", dir_name))
-    return(NULL)
-  }
-  list(base_name = match[1, 2], variant = match[1, 3], path = path)
-}
-
-all_data <- data.frame()
-
-for (arg in exp_args) {
-  info <- extract_info(arg)
-  if (is.null(info)) {
-    next
-  }
-
-  csv_file <- file.path(info$path, csv_suffix)
-  if (!file.exists(csv_file)) {
-    warning(paste("Missing:", csv_file))
-    next
-  }
-
-  df <- read_csv(csv_file, col_types = cols())
-  df$base_name <- info$base_name
-  df$variant <- info$variant
-  all_data <- bind_rows(all_data, df)
-}
-
-if (nrow(all_data) == 0) {
-  stop("No data loaded")
-}
-
-# Ignore OK_MARKER (only plot failures) and sum GROUP1_MARKER with TRAP
-# Also ignore mem benchmark
-all_data <- all_data |>
-  filter(resulttype %in% c("TRAP", "GROUP1_MARKER"), benchmark == "ip") |>
-  mutate(resulttype = ifelse(resulttype == "GROUP1_MARKER", "TRAP", resulttype))
-
-all_data <- all_data |>
-  group_by(base_name, variant, benchmark, resulttype) |>
-  summarise(faults = sum(faults), .groups = "drop")
-
-baseline <- all_data |> filter(variant == "c")
-comparisons <- all_data |> filter(variant != "c")
-
-ratios <- comparisons |>
-  left_join(
-    baseline |> select(base_name, benchmark, resulttype, faults),
-    by = c("base_name", "benchmark", "resulttype"),
-    suffix = c("", "_baseline")
-  ) |>
-  filter(!is.na(faults_baseline), faults_baseline > 0) |>
-  mutate(ratio = faults / faults_baseline)
-
-if (nrow(ratios) == 0) {
-  stop("No ratios computed (missing baseline or zero values)")
-}
-
-# Pivot to get aot and interp ratios side by side
-ratio_wide <- ratios |>
-  select(base_name, benchmark, resulttype, variant, ratio) |>
-  pivot_wider(names_from = variant, values_from = ratio) |>
-  filter(!is.na(aot), !is.na(interp))
-
-if (nrow(ratio_wide) == 0) {
-  stop("No paired aot/interp ratios found")
-}
-
-# Compute correlation
-cor_result <- cor(ratio_wide$aot, ratio_wide$interp, method = "pearson")
-cat(sprintf("Pearson correlation: %.4f\n", cor_result))
-
-# Create plot
-plot <- ggplot(
-  ratio_wide,
-  aes(x = aot, y = interp, color = base_name, shape = resulttype)
-) +
-  geom_point(size = 3, alpha = 0.7) +
-  scale_x_log10(name = "AOT / C Ratio") +
-  scale_y_log10(name = "Interpreter / C Ratio") +
-  labs(
-    title = sprintf("Ratio Correlation (r = %.4f)", cor_result),
-    color = "Experiment",
-    shape = "Marker"
-  ) +
-  theme_minimal() +
-  theme(
-    legend.position = "right",
-    plot.title = element_text(size = 14, face = "bold")
-  )
-
-ggsave(
-  "injections/ratio_correlation_customized.svg",
-  plot = plot,
-  width = 10,
-  height = 8
-)
-print("Saved ratio_correlation_customized.svg")

scripts/menu.pl

@@ -631,10 +631,13 @@ my %handlers = (
 
         # Need to know which chart uses which datafile
         my @faults_charts =
-          grep { /heatmap|scatter|sankey|instr_fault_correlation/ }
-          @selected_charts;
+          grep {
+/_heatmap|_scatter|_sankey|_instr_fault_correlation|_instr_fault_rate_heatmap|fault_rates_per_instruction/
+          } @selected_charts;
         my @resultsdata_charts =
-          grep { /result|combined_comparison|combined_ratio/ } @selected_charts;
+          grep {
+/_result|_fault_count_comparison|_ratio_comparison|_fault_count_correlation/
+          } @selected_charts;
 
         # Select if faults.csv or a filtered variant should be used
         my $faults_csv;

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__)