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util-read.R
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## This file is part of coronet, which is free software: you
## can redistribute it and/or modify it under the terms of the GNU General
## Public License as published by the Free Software Foundation, version 2.
##
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License along
## with this program; if not, write to the Free Software Foundation, Inc.,
## 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
##
## Copyright 2016-2019 by Claus Hunsen <hunsen@fim.uni-passau.de>
## Copyright 2017 by Raphael Nömmer <noemmer@fim.uni-passau.de>
## Copyright 2017-2018 by Christian Hechtl <hechtl@fim.uni-passau.de>
## Copyright 2020-2022, 2024 by Christian Hechtl <hechtl@cs.uni-saarland.de>
## Copyright 2017 by Felix Prasse <prassefe@fim.uni-passau.de>
## Copyright 2017-2018 by Thomas Bock <bockthom@fim.uni-passau.de>
## Copyright 2023-2024 by Thomas Bock <bockthom@cs.uni-saarland.de>
## Copyright 2018 by Jakob Kronawitter <kronawij@fim.uni-passau.de>
## Copyright 2018-2019 by Anselm Fehnker <fehnker@fim.uni-passau.de>
## Copyright 2020-2021, 2023 by Niklas Schneider <s8nlschn@stud.uni-saarland.de>
## Copyright 2021 by Johannes Hostert <s8johost@stud.uni-saarland.de>
## Copyright 2021 by Mirabdulla Yusifli <s8miyusi@stud.uni-saarland.de>
## Copyright 2022 by Jonathan Baumann <joba00002@stud.uni-saarland.de>
## Copyright 2022-2023 by Maximilian Löffler <s8maloef@stud.uni-saarland.de>
## Copyright 2024 by Leo Sendelbach <s8lesend@stud.uni-saarland.de>
## All Rights Reserved.
## Note:
## The definition of column names for each individual data source used in this file corresponds to the individual
## extraction process of the tool 'codeface-extraction' (https://github.com/se-sic/codeface-extraction; use
## commit 0700f94 or a compatible later commit).
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Libraries ---------------------------------------------------------------
requireNamespace("logging") # for logging
requireNamespace("parallel") # for parallel computation
requireNamespace("plyr")
requireNamespace("digest") # for sha1 hashing of IDs
requireNamespace("sqldf") # for SQL-selections on data.frames
requireNamespace("data.table") # for faster data.frame processing
requireNamespace("yaml") # for reading commit interaction data
requireNamespace("fastmap") # for fast implementation of a map
requireNamespace("purrr") # for fast mapping function
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Helper functions --------------------------------------------------------
#' Remove the "deleted user" or the author with empty name "" from a data frame.
#'
#' @param data the data from which to remove the "deleted user" and author with empty name.
#' @param columns the columns in which to search for the "deleted user" and author with empty name.
#' [default: c("author.name")]
#'
#' @return the data frame without the rows in which the author name is "deleted user" or ""
remove.deleted.and.empty.user = function(data, columns = c("author.name")) {
if (!all(columns %in% colnames(data))) {
logging::logerror("The given columns are not present in the data.frame.")
stop("Stopped due to invalid column names.")
}
## loop over the given columns and remove all rows in which the author name is "deleted user" or ""
for (column in columns) {
data = data[tolower(data[, column]) != "deleted user" & data[, column] != "", ]
}
return(data)
}
#' Concatenate function and file name, i.e. 'file::function'
#'
#' @param file.name the name of the file
#' @param function.name the name of the function
#'
#' @return the concatenated function name
prefix.function.with.file.name = function(file.name, function.name) {
return(paste(file.name, function.name, sep = "::"))
}
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Main data sources -------------------------------------------------------
## * Commit data -----------------------------------------------------------
## column names of a dataframe containing commits (see file 'commits.list' and function \code{read.commits})
COMMITS.LIST.COLUMNS = c(
"commit.id", # id
"date", "author.name", "author.email", # author information
"committer.date", "committer.name", "committer.email", # committer information
"hash", "changed.files", "added.lines", "deleted.lines", "diff.size", # commit information
"file", "artifact", "artifact.type", "artifact.diff.size" ## commit-dependency information
)
## declare the datatype for each column in the constant 'COMMITS.LIST.COLUMNS'
COMMITS.LIST.DATA.TYPES = c(
"character",
"POSIXct", "character", "character",
"POSIXct", "character", "character",
"character", "integer", "integer", "integer", "integer",
"character", "character", "character", "integer"
)
#' Read the commits from the 'commits.list' file.
#'
#' @param data.path the path to the commit list
#' @param artifact the artifact whose commits are read
#'
#' @return the read commits
read.commits = function(data.path, artifact) {
logging::logdebug("read.commits: starting.")
file = file.path(data.path, "commits.list")
## read data.frame from disk (as expected from save.list.to.file) [can be empty]
commit.data = try(read.table(file, header = FALSE, sep = ";", strip.white = TRUE,
encoding = "UTF-8"), silent = TRUE)
## handle the case that the list of commits is empty
if (inherits(commit.data, "try-error") || nrow(commit.data) < 1) {
logging::logwarn("There are no commits available for the current environment.")
logging::logwarn("Datapath: %s", data.path)
# return a dataframe with the correct columns but zero rows
return(create.empty.commits.list())
}
## assign prepared column names to the dataframe
colnames(commit.data) = COMMITS.LIST.COLUMNS
## remove duplicated lines (even if they contain different commit ids but the same commit hash)
commit.data = commit.data[rownames(unique(commit.data[, -1])), ]
## aggregate lines which are identical except for the "artifact.diff.size" column (ignoring the commit id)
## 1) select columns which have to be identical
primary.columns = COMMITS.LIST.COLUMNS[!(COMMITS.LIST.COLUMNS %in% c("commit.id", "artifact.diff.size"))]
## 2) aggregate "artifact.diff.size" for identical rows of the selected columns
commit.data.without.id = aggregate(commit.data["artifact.diff.size"],
commit.data[primary.columns],
function(sizes) { as.integer(round(mean(sizes))) })
## 3) keep only one commit id for identical rows of the selected columns
commit.data.without.artifact.diff.size = aggregate(commit.data["commit.id"],
commit.data[primary.columns],
min)
## 4) merge the data again to have both "commit.id" and "artifact.diff.size" in one data.frame again
commit.data = merge(commit.data.without.id, commit.data.without.artifact.diff.size)
## 5) reorder the columns of the data.frame as their order might be changed during aggregating and merging
commit.data = commit.data[, COMMITS.LIST.COLUMNS]
## Commits to files that are not tracked by Codeface have the empty string in the file and artifact column.
## To better indicate this, the 'file' column value is changed to 'untracked.file'.
commit.data["file"] = ifelse(commit.data[["file"]] == "", UNTRACKED.FILE, commit.data[["file"]])
## rewrite data.frame when we want file-based data
## (we have proximity-based data as foundation)
if (artifact == "file") {
## aggregate diff size by hash and file
commit.data = sqldf::sqldf("SELECT *, SUM(`artifact.diff.size`) AS diffsum
FROM `commit.data`
GROUP BY `hash`, `file`
ORDER BY `date`, `author.name`, `commit.id`, `file`, `artifact`")
## fix column class for diffsum
commit.data["diffsum"] = as.integer(commit.data[["diffsum"]])
## copy columns to match proper layout for further analyses
commit.data["artifact"] = commit.data[["file"]]
commit.data["artifact.type"] = ifelse(commit.data[["file"]] == UNTRACKED.FILE,
UNTRACKED.FILE.EMPTY.ARTIFACT.TYPE,
"File")
commit.data["artifact.diff.size"] = commit.data[["diffsum"]]
commit.data["diffsum"] = NULL # remove
}
## rewrite data.frame when we want function-based data
## (we have proximity-based data as foundation)
if (artifact == "function") {
## artifact = file name + "::" . function name
artifacts.new = prefix.function.with.file.name(commit.data[["file"]], commit.data[["artifact"]])
## clean up empty artifacts and File_Level artifact
artifacts.new = gsub("^::$", "", artifacts.new)
artifacts.new = gsub("^(.*)::File_Level$", "File_Level", artifacts.new)
## insert new artifact names into commit table
commit.data["artifact"] = artifacts.new
}
## Commits to files that are not tracked by Codeface have the empty string in the file, artifact, and
## artifact-type column. To better indicate this, the correpsonding column values are adapted.
commit.data["artifact"] = ifelse(commit.data[["artifact"]] == "",
UNTRACKED.FILE.EMPTY.ARTIFACT,
commit.data[["artifact"]])
commit.data["artifact.type"] = ifelse(commit.data[["artifact.type"]] == "",
UNTRACKED.FILE.EMPTY.ARTIFACT.TYPE,
commit.data[["artifact.type"]])
commit.data = remove.deleted.and.empty.user(commit.data, c("author.name", "committer.name")) # filter deleted user
## convert dates and sort by them
commit.data[["date"]] = get.date.from.string(commit.data[["date"]])
commit.data[["committer.date"]] = get.date.from.string(commit.data[["committer.date"]])
commit.data = commit.data[order(commit.data[["date"]], decreasing = FALSE), ] # sort!
## set pattern for commit ID for better recognition
commit.data[["commit.id"]] = format.commit.ids(commit.data[["commit.id"]])
row.names(commit.data) = seq_len(nrow(commit.data))
## check that dataframe is of correct shape
verify.data.frame.columns(commit.data, COMMITS.LIST.COLUMNS, COMMITS.LIST.DATA.TYPES)
## store the commit data
logging::logdebug("read.commits: finished.")
return(commit.data)
}
#' Create an empty dataframe which has the same shape as a dataframe containing commits. The dataframe has the column
#' names and column datatypes defined in \code{COMMITS.LIST.COLUMNS} and \code{COMMITS.LIST.DATA.TYPES}, respectively.
#'
#' @return the empty dataframe
create.empty.commits.list = function() {
return(create.empty.data.frame(COMMITS.LIST.COLUMNS, COMMITS.LIST.DATA.TYPES))
}
## * Mail data -------------------------------------------------------------
## column names of a dataframe containing mails (see file 'mails.list' and function \code{read.mails})
MAILS.LIST.COLUMNS = c(
"author.name", "author.email", # author information
"message.id", "date", "date.offset", "subject", # meta information
"thread", # thread ID
"artifact.type" # artifact type
)
## declare the datatype for each column in the constant 'MAILS.LIST.COLUMNS'
MAILS.LIST.DATA.TYPES = c(
"character", "character",
"character", "POSIXct", "integer", "character",
"character",
"character"
)
#' Read the mail data from the 'emails.list' file.
#'
#' @param data.path the path to the mail data
#'
#' @return the read mail data
read.mails = function(data.path) {
logging::logdebug("read.mails: starting.")
## get file name of commit data
file = file.path(data.path, "emails.list")
## read data.frame from disk (as expected from save.list.to.file) [can be empty]
mail.data = try(read.table(file, header = FALSE, sep = ";", strip.white = TRUE,
encoding = "UTF-8"), silent = TRUE)
## handle the case that the list of mails is empty
if (inherits(mail.data, "try-error") || nrow(mail.data) < 1) {
logging::logwarn("There are no mails available for the current environment.")
logging::logwarn("Datapath: %s", data.path)
return(create.empty.mails.list())
}
## set proper artifact type for proper vertex attribute 'artifact.type'
mail.data["artifact.type"] = "Mail"
colnames(mail.data) = MAILS.LIST.COLUMNS
## set pattern for thread ID for better recognition
mail.data[["thread"]] = sprintf("<thread-%s>", mail.data[["thread"]])
## remove mails without a proper date as they mess up directed mail-based networks
## this basically only applies for project-level analysis
empty.dates = which(mail.data[["date"]] == "" | is.na(mail.data[["date"]]))
if (length(empty.dates) > 0)
mail.data = mail.data[-empty.dates, ]
## convert dates and sort by them
mail.data[["date"]] = get.date.from.string(mail.data[["date"]])
mail.data = mail.data[order(mail.data[["date"]], decreasing = FALSE), ] # sort!
## remove all mails with dates before 1990-01-01 00:00:00
break.date = get.date.from.string("1970-01-01 00:00:00")
break.to.cut = mail.data[["date"]] < break.date
mail.data = mail.data[!break.to.cut, ]
if (sum(break.to.cut) > 0) {
logging::logwarn(
"Removed %s e-mail(s) after reading data file due to obiously wrong dates (before %s).",
sum(break.to.cut), break.date
)
}
mail.data = remove.deleted.and.empty.user(mail.data) # filter deleted user
## check that dataframe is of correct shape
verify.data.frame.columns(mail.data, MAILS.LIST.COLUMNS, MAILS.LIST.DATA.TYPES)
## store the mail data
logging::logdebug("read.mails: finished.")
return(mail.data)
}
#' Create an empty dataframe which has the same shape as a dataframe containing mails. The dataframe has the column
#' names and column datatypes defined in \code{MAILS.LIST.COLUMNS} and \code{MAILS.LIST.DATA.TYPES}, respectively.
#'
#' @return the empty dataframe
create.empty.mails.list = function() {
return(create.empty.data.frame(MAILS.LIST.COLUMNS, MAILS.LIST.DATA.TYPES))
}
## * Issue data ------------------------------------------------------------
## column names of a dataframe containing issues (see file 'issues.list' and function \code{read.issues})
ISSUES.LIST.COLUMNS = c(
"issue.id", "issue.title", "issue.type", "issue.state", "issue.resolution", "creation.date", "closing.date", "issue.components", # issue information
"event.name", # event type
"author.name", "author.email", # auhtor information
"date", "event.info.1", "event.info.2", "event.id", # event details
"issue.source", # source information
"artifact.type" # artifact type
)
## declare the datatype for each column in the constant 'ISSUES.LIST.COLUMNS'
ISSUES.LIST.DATA.TYPES = c(
"character", "character", "list()", "character", "list()", "POSIXct", "POSIXct", "list()",
"character",
"character", "character",
"POSIXct", "character", "list()", "character",
"character",
"character"
)
#' Read and parse the issue data from the 'issues.list' file.
#'
#' Note: The dates in the \code{"date"} column may be remapped to the creation date of the corresponding issue,
#' especially for \code{"commit_added"} events. This happens when the event has happened before the issue creation date.
#' The original date of these events can always be found in the \code{"event.info.2"} column.
#'
#' @param data.path the path to the issue data
#' @param issues.sources the sources of the issue data. One or both of \code{"jira"} and \code{"github"}.
#'
#' @return the read and parsed issue data
read.issues = function(data.path, issues.sources = c("jira", "github")) {
logging::logdebug("read.issues: starting.")
## check arguments
issues.sources = match.arg(arg = issues.sources, several.ok = TRUE)
## read data from chosen sources
issue.data = lapply(issues.sources, function(issue.source) {
## get file name of source issue data
filepath = file.path(data.path, sprintf("issues-%s.list", issue.source))
## read source issues from disk [can be empty]
source.data = try(read.table(filepath, header = FALSE, sep = ";", strip.white = TRUE,
encoding = "UTF-8"), silent = TRUE)
## handle the case that the list of issues is empty
if (inherits(source.data, "try-error") || nrow(source.data) < 1) {
logging::logwarn("There are no %s issue data available for the current environment.", issue.source)
logging::logwarn("Datapath: %s", data.path)
return(create.empty.issues.list())
}
## create (now empty) column 'event.id' to properly set column names
## (this column is reset later)
source.data[["event.id"]] = NA
## add source column to data
source.data["issue.source"] = issue.source
## set proper artifact type for proper vertex attribute 'artifact.type'
source.data["artifact.type"] = "IssueEvent"
## set proper column names
colnames(source.data) = ISSUES.LIST.COLUMNS
return(source.data)
})
## combine issue data from all sources
issue.data = do.call(rbind, issue.data)
## if no chosen source is present exit early by returning the (combined) empty issues list
if (nrow(issue.data) < 1) {
return(issue.data)
}
## set pattern for issue ID for better recognition
issue.data[["issue.id"]] = sprintf(ISSUE.ID.FORMAT, issue.data[["issue.source"]], issue.data[["issue.id"]])
## properly parse and store data in list-type columns
issue.data[["issue.type"]] = I(unname(lapply(issue.data[["issue.type"]], jsonlite::fromJSON, simplifyVector = FALSE)))
issue.data[["issue.resolution"]] = I(unname(lapply(issue.data[["issue.resolution"]], jsonlite::fromJSON, simplifyVector = FALSE)))
issue.data[["issue.components"]] = I(unname(lapply(issue.data[["issue.components"]], jsonlite::fromJSON, simplifyVector = FALSE)))
issue.data[["event.info.2"]] = I(unname(lapply(issue.data[["event.info.2"]], jsonlite::fromJSON, simplifyVector = FALSE)))
## convert dates and sort by 'date' column
issue.data[["date"]] = get.date.from.string(issue.data[["date"]])
issue.data[["creation.date"]] = get.date.from.string(issue.data[["creation.date"]])
issue.data[["closing.date"]] = get.date.from.string(issue.data[["closing.date"]])
## if other issues are referenced, convert names to ID format
matches = issue.data[issue.data[["event.name"]] %in% c("add_link", "remove_link", "referenced_by") &
issue.data[["event.info.2"]] == "issue", ]
formatted.matches = sprintf(ISSUE.ID.FORMAT, matches[["issue.source"]], matches[["event.info.1"]])
issue.data[issue.data[["event.name"]] %in% c("add_link", "remove_link", "referenced_by") &
issue.data[["event.info.2"]] == "issue", ][["event.info.1"]] = formatted.matches
if (nrow(issue.data) > 0) {
## fix all dates to be after the creation date
## violations can happen for "commit_added" events if the commit was made before the PR was opened
## the original date for "commit_added" events is stored in "event.info.2" in any case
commit.added.events = issue.data[["event.name"]] == "commit_added"
issue.data[commit.added.events, "event.info.2"] = get.date.string(issue.data[commit.added.events, "date"])
commit.added.events.before.creation = commit.added.events &
!is.na(issue.data["creation.date"]) & (issue.data["date"] < issue.data["creation.date"])
issue.data[commit.added.events.before.creation, "date"] = issue.data[commit.added.events.before.creation, "creation.date"]
## filter deleted user from the "author.name" column,
## however, keep events where the user in the "event.info.1" column is empty or deleted
issue.data = remove.deleted.and.empty.user(issue.data)
issue.data = issue.data[order(issue.data[["date"]], decreasing = FALSE), ] # sort!
}
## generate a unique event ID from issue ID, author, and date
issue.data[["event.id"]] = sapply(
paste(issue.data[["issue.id"]], issue.data[["author.name"]], issue.data[["date"]], sep = "_"),
function(event) { digest::digest(event, algo="sha1", serialize = FALSE) }
)
## check that dataframe is of correct shape
verify.data.frame.columns(issue.data, ISSUES.LIST.COLUMNS, ISSUES.LIST.DATA.TYPES)
logging::logdebug("read.issues: finished.")
return(issue.data)
}
#' Create an empty dataframe which has the same shape as a dataframe containing issues. The dataframe has the column
#' names and column datatypes defined in \code{ISSUES.LIST.COLUMNS} and \code{ISSUES.LIST.DATA.TYPES}, respectively.
#'
#' @return the empty dataframe
create.empty.issues.list = function() {
return(create.empty.data.frame(ISSUES.LIST.COLUMNS, ISSUES.LIST.DATA.TYPES))
}
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Additional data sources -------------------------------------------------
## * Author data -----------------------------------------------------------
## column names of a data frame containing bot information (see file
## 'bots.list' and function \code{read.bot.list})
BOT.LIST.COLUMNS = c(
"author.name", "author.email", ## author
"is.bot" ## whether this is a bot
)
#' Read the bot classification from the 'bots.list' file.
#'
#' @param data.path the path to the commit-messages list
#'
#' @return a data frame with author.name, author.email, and a (potentially NA) boolean whether this is a bot,
#' or \code{NULL} if the above file is not present.
read.bot.info = function(data.path) {
logging::logdebug("read.bot.info: starting.")
## read the file with the bot info
file = file.path(data.path, "bots.list")
bot.data = try(read.table(file, header = FALSE, sep = ";", strip.white = TRUE,
encoding = "UTF-8"), silent = TRUE)
## handle the case that the bot info is empty
if (inherits(bot.data, "try-error")) {
logging::logwarn("There is no bot information available for the current environment.")
logging::logwarn("Datapath: %s", data.path)
## return NULL. Creating an empty dataframe is not possible
## because no type information about bot information is present
return(NULL)
}
## set column names for new data frame
colnames(bot.data) = BOT.LIST.COLUMNS
bot.data["is.bot"] = sapply(bot.data[["is.bot"]], function(x) switch(x, Bot = TRUE, Human = FALSE, NA))
## check that dataframe is of correct shape
verify.data.frame.columns(bot.data, BOT.LIST.COLUMNS)
logging::logdebug("read.bot.info: finished.")
return(bot.data)
}
## column names of a dataframe containing authors (see file 'authors.list' and function \code{read.authors})
AUTHORS.LIST.COLUMNS = c(
"author.id", "author.name", "author.email", "is.bot"
)
## column names of a dataframe containing authors, before adding bot data.
AUTHORS.LIST.COLUMNS.WITHOUT.BOTS = AUTHORS.LIST.COLUMNS[1:3]
## declare the datatype for each column in the constant 'AUTHORS.LIST.COLUMNS'
AUTHORS.LIST.DATA.TYPES = c(
"character", "character", "character", "logical"
)
#' Read the author data from the 'authors.list' file.
#'
#' @param data.path the path to the author data
#'
#' @return the read author data
read.authors = function(data.path) {
logging::logdebug("read.authors: starting.")
## get file name of commit data
file = file.path(data.path, "authors.list")
## read data.frame from disk (as expected from save.list.to.file)
authors.df = try(read.table(file, header = FALSE, sep = ";", strip.white = TRUE,
encoding = "UTF-8"), silent = TRUE)
## break if the list of authors is empty
if (inherits(authors.df, "try-error") || nrow(authors.df) < 1) {
logging::logerror("There are no authors available for the current environment.")
logging::logwarn("Datapath: %s", data.path)
stop("Stopped due to missing authors.")
}
## if there is no third column, we need to add e-mail-address dummy data
if (ncol(authors.df) != length(AUTHORS.LIST.COLUMNS.WITHOUT.BOTS)) {
authors.df[3] = ""
}
colnames(authors.df) = AUTHORS.LIST.COLUMNS.WITHOUT.BOTS
bot.data = read.bot.info(data.path)
if (!is.null(bot.data)) {
authors.df = merge(authors.df, bot.data, by = c("author.name", "author.email"), all.x = TRUE, sort = FALSE)
authors.df = authors.df[order(authors.df[["author.id"]]), ] # re-order after read
row.names(authors.df) = seq_len(nrow(authors.df))
} else {
## if bot data is not available, add NA data, which is what would have happened
## if the file was empty
authors.df[["is.bot"]] = NA
}
## re-order the columns
authors.df = authors.df[, AUTHORS.LIST.COLUMNS]
authors.df = remove.deleted.and.empty.user(authors.df)
## assure type correctness
authors.df[["author.id"]] = as.character(authors.df[["author.id"]])
## check that dataframe is of correct shape
verify.data.frame.columns(authors.df, AUTHORS.LIST.COLUMNS, AUTHORS.LIST.DATA.TYPES)
## store the ID--author mapping
logging::logdebug("read.authors: finished.")
return(authors.df)
}
#' Create an empty dataframe which has the same shape as a dataframe containing authors. The dataframe has the column
#' names and column datatypes defined in \code{AUTHORS.LIST.COLUMNS} and \code{AUTHORS.LIST.DATA.TYPES}, respectively.
#'
#' @return the empty dataframe
create.empty.authors.list = function() {
return(create.empty.data.frame(AUTHORS.LIST.COLUMNS, AUTHORS.LIST.DATA.TYPES))
}
## * Gender data ------------------------------------------------------------
## column names of a dataframe containing gender data (see function \code{read.gender})
GENDER.LIST.COLUMNS = c(
"author.name", "gender"
)
## declare the datatype for each column in the constant 'GENDER.LIST.COLUMNS'
GENDER.LIST.DATA.TYPES = c(
"character", "character"
)
## declare predefined values for the gender column
GENDER.LIST.VALUES = c(
"male", "female", "unknown"
)
#' Read and parse the gender data from the 'gender' file.
#' The parsed form is a data frame with author.name as key, gender as value.
#'
#' @param data.path the path to the gender data
#'
#' @return the read and parsed gender data
read.gender = function(data.path) {
## get file name of the gender data
file = file.path(data.path, "gender.list")
## read data.frame from disk (as expected from save.list.to.file) [can be empty]
## comment char is set to empty string as the names of developers can contain the
## char '#'. This does not affect the other data sources as all names there are
## in "".
gender.data = try(read.table(file, header = FALSE, sep = ";", strip.white = TRUE,
encoding = "UTF-8", comment.char = ""), silent = TRUE)
## handle the case if the list of items is empty
if (inherits(gender.data, "try-error") || nrow(gender.data) < 1) {
logging::logwarn("There are no gender data available for the current environment.")
logging::logwarn("Datapath: %s", data.path)
return(create.empty.gender.list())
}
colnames(gender.data) = GENDER.LIST.COLUMNS
## check whether there are undefined gender labels
undefined.labels = setdiff(gender.data[["gender"]], GENDER.LIST.VALUES)
if (length(undefined.labels) > 0){
## find authors who have undefined gender labels
undefined.labels.authors = filter(gender.data, gender %in% undefined.labels)
logging::logwarn(sprintf("Undefined gender labels. %s cannot be used. Only %s are allowed.
The following authors have undefined labels: %s ",
paste(shQuote(undefined.labels), collapse = ","),
paste(shQuote(GENDER.LIST.VALUES), collapse = ", "),
paste(shQuote(undefined.labels.authors[["author.name"]]), collapse = ",")))
## replace all undefined labels with 'unknown'
gender.data[["gender"]][gender.data[["gender"]] %in% undefined.labels] = "unknown"
logging::logwarn("Undefined gender labels have been replaced with 'unknown'.")
}
## replace all 'unknown' values with NA
gender.data[["gender"]][gender.data[["gender"]] == "unknown"] = NA
gender.data = gender.data[order(gender.data[["author.name"]]), ] # re-order after read
## remove rownames
rownames(gender.data) = NULL
## check that dataframe is of correct shape
verify.data.frame.columns(gender.data, GENDER.LIST.COLUMNS, GENDER.LIST.DATA.TYPES)
logging::logdebug("read.gender: finished.")
return(gender.data)
}
#' Create an empty dataframe which has the same shape as a dataframe containing gender data.
#' The dataframe has the column names and column datatypes defined in \code{GENDER.LIST.COLUMNS}
#' and \code{GENDER.LIST.DATA.TYPES}, respectively.
#'
#' @return the empty dataframe
create.empty.gender.list = function() {
return(create.empty.data.frame(GENDER.LIST.COLUMNS, GENDER.LIST.DATA.TYPES))
}
## * Commit message data ---------------------------------------------------
## column names of a dataframe containing commit messages (see file
## 'commitMessages.list' and function \code{read.commit.messages})
COMMIT.MESSAGE.LIST.COLUMNS = c(
"commit.id", # id
"hash", "title", "message"
)
## declare the datatype for each column in the constant 'COMMIT.MESSAGE.LIST.COLUMNS'
COMMIT.MESSAGE.LIST.DATA.TYPES = c(
"character",
"character", "character", "character"
)
## declare the constant (5 spaces) which is used by codeface to separate lines in
## commit messages
COMMIT.MESSAGE.LINE.SEP.CODEFACE = paste0(rep(" ", 5), collapse = "")
## declare the constant to how line breaks should look like in the data
COMMIT.MESSAGE.LINE.SEP.REPLACE = "\n"
#' Read the commit messages from the 'commitMessages.list' file.
#' Turn line breaks represented with five spaces into \n line breaks and
#' ignore initial spaces. Also remove spaces at the beginning and the end of
#' the message.
#'
#' @param data.path the path to the commit-messages list
#'
#' @return a data frame with id, hash, title and message body´
read.commit.messages = function(data.path) {
logging::logdebug("read.commit.messages: starting.")
## read the file with the commit messages
file = file.path(data.path, "commitMessages.list")
commit.message.data = try(read.table(file, header = FALSE, sep = ";", strip.white = TRUE,
encoding = "UTF-8"), silent = TRUE)
## handle the case that the list of commits is empty
if (inherits(commit.message.data, "try-error") || nrow(commit.message.data) < 1) {
logging::logwarn("There are no commit messages available for the current environment.")
logging::logwarn("Datapath: %s", data.path)
## return a dataframe with the correct columns but zero rows
return(create.empty.commit.message.list())
}
## set column names for new data frame; unprocessed data only has three columns so omit the "title" column
colnames(commit.message.data) = COMMIT.MESSAGE.LIST.COLUMNS[COMMIT.MESSAGE.LIST.COLUMNS != "title"]
## split the message string with the new line symbol
message.split = strsplit(commit.message.data[["message"]], COMMIT.MESSAGE.LINE.SEP.CODEFACE)
## prepare the 'message.split' object so that it contains a two-element vector for each commit
message.split.df = lapply(message.split, function(tuple) {
## clear the message from empty lines
lines = tuple[tuple != ""]
## remove spaces before first line
lines = gsub("^\\s+", "", lines)
## remove spaces at the end of the message
lines = gsub("\\s+$", "", lines)
## set title and message empty in case there was no actual commit message or it was consisting of spaces only
title = ""
message = ""
## if there is only one line, create an empty body
if (length(lines) == 1) {
title = lines[[1]]
}
## if there are more than two lines, merge all except for the first one
else if (length(lines) >= 2) {
title = lines[[1]]
## use an ascii line break instead
message = paste(tail(lines, -1), collapse = COMMIT.MESSAGE.LINE.SEP.REPLACE)
}
return(data.table::data.table(title = title, message = message))
})
## convert to a data.table with two columns
message.split.df = data.table::rbindlist(message.split.df)
## create a data frame containing all four necessary columns
commit.message.data["title"] = message.split.df[["title"]] # title
commit.message.data["message"] = message.split.df[["message"]] # message
## reorder columns because they are added alphabetically
commit.message.data = commit.message.data[, COMMIT.MESSAGE.LIST.COLUMNS]
## Make commit.id have numeric type and set row names
commit.message.data[["commit.id"]] = format.commit.ids(commit.message.data[["commit.id"]])
row.names(commit.message.data) = seq_len(nrow(commit.message.data))
## check that dataframe is of correct shape
verify.data.frame.columns(commit.message.data, COMMIT.MESSAGE.LIST.COLUMNS, COMMIT.MESSAGE.LIST.DATA.TYPES)
logging::logdebug("read.commit.messages: finished.")
return(commit.message.data)
}
#' Create a empty dataframe which has the same shape as a dataframe containing commit messages.
#' The dataframe has the column names and column datatypes defined in \code{COMMIT.MESSAGE.LIST.COLUMNS} and
#' \code{COMMIT.MESSAGE.LIST.DATA.TYPES}, respectively.
#'
#' @return the empty dataframe
create.empty.commit.message.list = function() {
return(create.empty.data.frame(COMMIT.MESSAGE.LIST.COLUMNS, COMMIT.MESSAGE.LIST.DATA.TYPES))
}
## * PaStA data ------------------------------------------------------------
## column names of a dataframe containing PaStA data (see function \code{read.pasta})
PASTA.LIST.COLUMNS = c(
"message.id", "commit.hash", "revision.set.id"
)
## declare the datatype for each column in the constant 'PASTA.LIST.COLUMNS'
PASTA.LIST.DATA.TYPES = c(
"character", "character", "character"
)
#' Read and parse the PaStA data from the 'patch-groups' file.
#' The form in the file is : <message-id> <possibly another message.id> ... => commit.hash commit.hash2 ....
#' The parsed form is a data frame with message IDs as keys, commit hashes as values, and a revision set id.
#' If the message ID does not get mapped to a commit hash, the value for the commit hash is \code{NA}.
#'
#' @param data.path the path to the PaStA data
#'
#' @return the read and parsed PaStA data
read.pasta = function(data.path) {
# constant for seperating keys and value
SEPERATOR = " => "
KEY.SEPERATOR = " "
## get file name of PaStA data
filepath = file.path(data.path, "patch-groups")
## read data from disk [can be empty]
lines = suppressWarnings(try(readLines(filepath), silent = TRUE))
## handle the case if the list of PaStA items is empty
if (inherits(lines, "try-error") || length(lines) < 1) {
logging::logwarn("There are no PaStA data available for the current environment.")
logging::logwarn("Datapath: %s", data.path)
return(create.empty.pasta.list())
}
result.list = parallel::mcmapply(lines, seq_along(lines), SIMPLIFY = FALSE, FUN = function(line, line.id) {
#line = lines[i]
if ( nchar(line) == 0 ) {
return(NULL)
}
if (!grepl("<", line)) {
return(NULL)
}
# 1) split at arrow
# 2) split keys
# 3) split values
# 4) insert all key-value pairs by iteration (works also if there is only one key)
if (grepl(SEPERATOR, line)) {
line.split = unlist(strsplit(line, SEPERATOR))
keys = line.split[1]
values = line.split[2]
keys.split = unlist(strsplit(keys, KEY.SEPERATOR))
values.split = unlist(strsplit(values, KEY.SEPERATOR))
} else {
keys.split = unlist(strsplit(line, KEY.SEPERATOR))
values.split = NA
}
# Transform data to data.frame
df = merge(keys.split, values.split)
colnames(df) = c("message.id", "commit.hash")
df["revision.set.id"] = sprintf("<revision-set-%s>", line.id)
return(df)
})
result.df = plyr::rbind.fill(result.list)
## check that dataframe is of correct shape
verify.data.frame.columns(result.df, PASTA.LIST.COLUMNS, PASTA.LIST.DATA.TYPES)
logging::logdebug("read.pasta: finished.")
return(result.df)
}
#' Create an empty dataframe which has the same shape as a dataframe containing PaStA data.
#' The dataframe has the column names and column datatypes defined in \code{PASTA.LIST.COLUMNS}
#' and \code{PASTA.LIST.DATA.TYPES}, respectively.
#'
#' @return the empty dataframe
create.empty.pasta.list = function() {
return(create.empty.data.frame(PASTA.LIST.COLUMNS, PASTA.LIST.DATA.TYPES))
}
## * Commit interaction data -----------------------------------------------
## column names of a dataframe containing commit interaction data (see function \code{read.commit.interactions})
COMMIT.INTERACTION.LIST.COLUMNS = c(
"func", "commit.hash", "file",
"base.hash", "base.func", "base.file",
"base.author", "interacting.author",
"artifact.type"
)
## declare the datatype for each column in the constant 'COMMIT.INTERACTION.LIST.COLUMNS'
COMMIT.INTERACTION.LIST.DATA.TYPES = c(
"character", "character", "character",
"character", "character", "character",
"character", "character", "character"
)
COMMIT.INTERACTION.GLOBAL.FILE.FUNCTION.NAME = "GLOBAL"
#' Read and parse the commit-interaction data. This data is present in a `.yaml` file which
#' needs to be broken down. Within the yaml file, there are different lists in which each
#' commit (hash) gets mapped to all commits it interacts with and the file/function because of
#' which they interact.
#'
#' @param data.path the path to the commit-interaction data
#'
#' @return the read and parsed commit-interaction data
read.commit.interactions = function(data.path = NULL) {
file = file.path(data.path, "commit-interactions.yaml")
commit.interaction.base = try(yaml::read_yaml(file = file,
handlers = list(int = function(x) {as.character(x)})),
silent = TRUE)
## handle the case that the list of commit-interactions is empty
if (inherits(commit.interaction.base, "try-error")) {
logging::logwarn("There are no commit-interactions available for the current environment.")
logging::logwarn("Datapath: %s", data.path)
# return a dataframe with the correct columns but zero rows
return(create.empty.commit.interaction.list())
}
## extract the top level list of the yaml file which is called 'result-map'
result.map = commit.interaction.base[["result-map"]]
## extract a mapping of functions to files to be able to determine what file the current interaction is
## based on
## 1) create an empty map
file.name.map = fastmap::fastmap()
## 2) create a mapping between functions and files as a named list
## which can be directly converted to a map
function.file.list = purrr::map(result.map, "file")
## 3) set the map using the list
file.name.map$mset(.list = function.file.list)
list.names = names(result.map)
## build the result dataframe by iterating over the 'result-map' list
commit.interaction.data = data.table::setDF(data.table::rbindlist(
parallel::mcmapply(result.map,
list.names,
SIMPLIFY = FALSE,
FUN = function(current.interaction, function.name) {
## get all commits that interact with the current one
insts = current.interaction[["insts"]]
interactions = data.table::setDF(data.table::rbindlist(lapply(insts, function(current.inst) {
base.hash = current.inst[["base-hash"]][["commit"]]
interacting.hashes = current.inst[["interacting-hashes"]]
interacting.hashes.df = data.table::setDF(data.table::rbindlist(lapply(interacting.hashes, function(hash) {
## if there is no function name in the current interaction, we set the function name to 'GLOBAL'
## as this is most likely code outside of functions, else we set the function name
if (!"function" %in% names(hash)) {
return(data.frame(func = COMMIT.INTERACTION.GLOBAL.FILE.FUNCTION.NAME,
commit.hash = hash[["commit"]],
file = COMMIT.INTERACTION.GLOBAL.FILE.FUNCTION.NAME))
} else if (is.null(file.name.map$get(hash[["function"]]))) {
## This case should never occur if the data was generated correctly!
warning("An interacting hash specifies a function that does not exist in the data!")
return(data.frame(matrix(nrow = 3, ncol = 0)))
} else {
file.name = file.name.map$get(hash[["function"]])
func.name = prefix.function.with.file.name(file.name, hash[("function")])
return(data.frame(func = func.name, commit.hash = hash[["commit"]], file = file.name))
}
})))
base.file.name = file.name.map$get(function.name)
interacting.hashes.df[["base.hash"]] = base.hash
interacting.hashes.df[["base.func"]] = prefix.function.with.file.name(base.file.name, function.name)
interacting.hashes.df[["base.file"]] = base.file.name
return(interacting.hashes.df)
})))
## Return an empty data frame if the current interaction is empty (i.e., if an interaction is not existent).
if (nrow(interactions) == 0) {
return(data.frame())
}
## Initialize author data as 'NA', since it is not available from the commit-interaction data.
## Author data will be merged from commit data in \code{update.commit.interactions}.
interactions["base.author"] = NA_character_
interactions["interacting.author"] = NA_character_
interactions["artifact.type"] = ARTIFACT.COMMIT.INTERACTION
return(interactions)
})))
## remove all duplicate entries from the resulting dataframe
commit.interaction.data = commit.interaction.data[!duplicated(commit.interaction.data), ]
verify.data.frame.columns(commit.interaction.data, COMMIT.INTERACTION.LIST.COLUMNS, COMMIT.INTERACTION.LIST.DATA.TYPES)
return(commit.interaction.data)
}
#' Create an empty dataframe which has the same shape as a dataframe containing commit interaction data.
#' The dataframe has the column names and column datatypes defined in \code{COMMIT.INTERACTION.LIST.COLUMNS}
#' and \code{COMMIT.INTERACTION.LIST.DATA.TYPES}, respectively.
#'
#' @return the empty dataframe
create.empty.commit.interaction.list = function() {
return (create.empty.data.frame(COMMIT.INTERACTION.LIST.COLUMNS, COMMIT.INTERACTION.LIST.DATA.TYPES))
}
## * Synchronicity data ----------------------------------------------------
## column names of a dataframe containing synchronicity data (see function \code{read.synchronicity})
SYNCHRONICITY.LIST.COLUMNS = c(
"hash", "synchronicity"
)
## declare the datatype for each column in the constant 'SYNCHRONICITY.LIST.COLUMNS'
SYNCHRONICITY.LIST.DATA.TYPES = c(
"character", "logical"
)
#' Read the synchronicity data from file. The name of the file follows
#' the following pattern: 'commit_sync_analysis_artifact_time.window.dat',
#' where artifact and time.window are the given variables.
#'
#' @param data.path the path to the synchronicity data
#' @param artifact the artifact whose synchronicity data get read
#' @param time.window the time window of the data to be read
#'
#' @return the read synchronicity data
read.synchronicity = function(data.path, artifact, time.window) {