Export residuals

analysis/generate_paper.sh

@@ -59,7 +59,7 @@ main() {
   # RQ1
   #
   Rscript analysis/paper/performance_distribution.R "${TMP_DIR}/decomposition_scores.csv" "${PAPER_REPOSITORY}/figures/rq1-performance-distribution.pdf"
-  Rscript analysis/paper/statistical_analysis_untangling_tool.R "${TMP_DIR}/decomposition_scores.csv" "${PAPER_REPOSITORY}/data/rq1.txt"
+  Rscript src/r/main/statistical_analysis_untangling_tool.R "${TMP_DIR}/decomposition_scores.csv" "${PAPER_REPOSITORY}/data/"
   Rscript analysis/paper/compare_models.R "${TMP_DIR}/decomposition_scores.csv" "${PAPER_REPOSITORY}/tables/model-comparison.tex" > "${PAPER_REPOSITORY}"/data/model-comparison.txt
 
   #

src/r/main/statistical_analysis_untangling_tool.R

@@ -0,0 +1,64 @@
+#!/usr/bin/env Rscript
+#
+# Run and export the statistical analysis for the untangling tools with p-value, Cohen's d, R^2, and model residuals normality test results.
+# The statistical analysis results are saved in two files:
+# - `outputDir/untangling_tool_performance_statistical_analysis.txt` contains the summary of the model, ANOVA, Shapiro-Wilk normality test, and Cohen's d.
+# - `outputDir/untangling_tool_performance_residuals.pdf` contains the residual plots of the model.
+#
+# Arguments:
+# - 1: The untangling performance file `decomposition_scores.csv` containing the performance of the untangling tools for a dataset.
+# - 1: The path to the directory where the analysis results will be saved.
+#
+# Output:
+# The results are saved as text data. The output file contains
+# the output of the summary() and cohen.d() functions.
+
+args = commandArgs(trailingOnly=TRUE)
+
+if (length(args)!=2) {
+  stop("Please provide the untangling performance file and the path where to store the results. Example: 'statistical_analysis_untangling_tool.R tool_performance.csv analysis/'", call.=FALSE)
+}
+untanglingPerformanceFile = args[1]
+outputDir= args[2]
+
+residuals_plot_file = paste(outputDir, "untangling_tool_performance_residuals.pdf", sep="/")
+untangling_tool_performance_statistical_analysis_file = paste(outputDir, "untangling_tool_performance_statistical_analysis.txt", sep="/")
+
+library(librarian)
+library(tidyverse)
+library(car)
+library(ggpubr)
+library(lme4)
+library(effsize)
+library(lmerTest)
+library(flexplot)
+library(rsq)
+shelf(broom)
+
+data <- read.csv(untanglingPerformanceFile, header = FALSE, col.names = c('Project', 'BugID', 'SmartCommit', 'Flexeme', 'FileUntangling'))
+data <- subset(data, select = -c(FileUntangling))
+data$BugID <- as_factor(data$BugID)
+
+# Convert to long format and select only SmartCommit and Flexeme to compare.
+data_long = pivot_longer(data, cols = c('SmartCommit', 'Flexeme'), names_to = 'Tool', values_to = 'Performance')
+
+# The summary can be interpreted as follows:
+# Intercept row shows whether the baseline treatment (whichever is first) is significantly different from 0.
+# The second row, containing the other treatment, shows whether the other treatment is significantly
+# different from the intercept.
+model <- lm(Performance ~ Tool, data=data_long)
+
+# Residuals
+# It is recommended to look at the residuals to check for normality rather than apply a statistical test.
+pdf(residuals_plot_file)
+visualize(model, "residuals")
+dev.off()
+
+# Analysis results
+sink(untangling_tool_performance_statistical_analysis_file)
+summary(model)
+anova(model)
+shapiro.test(residuals(model))
+cohen.d(data_long$Performance[data_long$Tool == "SmartCommit"], data_long$Performance[data_long$Tool == "Flexeme"])
+sink()
+