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util-core-peripheral.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 2017 by Mitchell Joblin <mitchell.joblin@uni-passau.de>
## Copyright 2017 by Ferdinand Frank <frankfer@fim.uni-passau.de>
## Copyright 2017 by Sofie Kemper <kemperso@fim.uni-passau.de>
## Copyright 2017-2020 by Claus Hunsen <hunsen@fim.uni-passau.de>
## Copyright 2017 by Felix Prasse <prassefe@fim.uni-passau.de>
## Copyright 2018-2019 by Christian Hechtl <hechtl@fim.uni-passau.de>
## Copyright 2021 by Christian Hechtl <hechtl@cs.uni-saarland.de>
## Copyright 2018 by Klara Schlüter <schluete@fim.uni-passau.de>
## Copyright 2019 by Thomas Bock <bockthom@fim.uni-passau.de>
## Copyright 2019 by Jakob Kronawitter <kronawij@fim.uni-passau.de>
## Copyright 2021 by Johannes Hostert <s8johost@stud.uni-saarland.de>
## Copyright 2024-2025 by Leo Sendelbach <s8lesend@stud.uni-saarland.de>
## All Rights Reserved.
##
## This file is derived from following Codeface script:
## https://github.com/siemens/codeface/blob/master/codeface/R/developer_classification.r
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Libraries ---------------------------------------------------------------
requireNamespace("sqldf") # for SQL-selections on data.frames
requireNamespace("igraph") # for calculation of network metrics (degree, eigen-centrality)
requireNamespace("markovchain") # for role stability analysis
requireNamespace("logging") # for logging
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Thresholds --------------------------------------------------------------
## Defines at which percentage of the work load authors will
## be classified as core
CORE.THRESHOLD = 0.8
## Defines the percentage of version development ranges in which
## an author has to be classified as core to be stated as a
## longterm core author
LONGTERM.CORE.THRESHOLD = 0.5
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Classification Type Categories ----------------------------------------
## Mapping of the classification type to its category which can either
## be 'network' or 'count' based categories
CLASSIFICATION.TYPE.TO.CATEGORY = list(
"network.degree" = "network",
"network.eigen" = "network",
"network.hierarchy" = "network",
"network.betweenness" = "network",
"network.closeness" = "network",
"network.pagerank" = "network",
"network.eccentricity" = "network",
"commit.count" = "count",
"loc.count" = "count",
"mail.count" = "count",
"mail.thread.count" = "count",
"issue.count" = "count",
"issue.comment.count" = "count",
"issue.commented.in.count" = "count",
"issue.created.count" = "count"
)
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Author-class wrappers and overviews -------------------------------------
#' Classify authors into the classes "core" and "peripheral".
#'
#' The classification algorithm works by considering a numerical value for each author that denotes their centrality
#' and can be imagined to work in the following way:
#' 1. Order the authors by their centrality value and put them into a stack in which the most central authors reside
#' on the top
#' 2. Initialize an empty set of "core" authors
#' 3. Pop the author residing at the very top of the stack and add them to the set of "core" authors
#' 4. Check if the combined centrality of the set of "core" authors is greater than or equal to \code{CORE.THRESHOLD}
#' times the total centrality value (sum over the centrality values of all authors)
#' - If so, terminate. Consider everyone in the set of "core" authors as "core" and everyone else as "peripheral".
#' - If not, go to step 3
#'
#' @param network the network containing the authors to classify (parameter is required if the parameter \code{type}
#' specifies a network-based classification metric) [default: NULL]
#' @param proj.data the \code{ProjectData} containing the authors to classify (parameter is required if the parameter
#' \code{type} specifies a count-based classification metric) [default: NULL]
#' @param type a character string declaring the classification metric. The classification metric determines which
#' numerical characteristic of authors is chosen as their centrality value.
#' The parameter currently supports the following eleven options:
#' Network-based options/metrics (parameter \code{network} has to be specified):
#' - "network.degree"
#' - "network.eigen"
#' - "network.hierarchy"
#' Count-based options/metrics (parameter \code{proj.data} has to be specified):
#' - "commit.count"
#' - "loc.count"
#' - "mail.count"
#' - "mail.thread.count"
#' - "issue.count"
#' - "issue.comment.count"
#' - "issue.commented.in.count"
#' - "issue.created.count"
#' [default: "network.degree"]
#' @param issue.type which issue type to consider for count-based metrics using issues
#' (see \code{preprocess.issue.data}). One of \code{"issues"},
#' \code{"pull.requests"} or \code{"all"}. This parameter is ignored for
#' non-issue-related classification metrics. [default: "all"]
#' @param result.limit the maximum number of authors contained in the classification result. Only the top
#' \code{result.limit} authors of the classification stack will be contained within the returned
#' classification result. \code{NULL} means that all authors will be returned. [default: NULL]
#' @param restrict.classification.to.authors a vector of author names. Only authors that are contained within this
#' vector are to be classified. Authors that appear in the vector but are not
#' part of the classification result (i.e., they are not present in the
#' underlying data) will be added to it afterwards (with a centrality value
#' of \code{NA}). \code{NULL} means that no restriction is made.
#' [default: NULL]
#' @return the classification result, that is, a list containing two named list members \code{core} and
#' \code{peripheral}, each of which holding the authors classified as core or peripheral, respectively. Both
#' entries in this list (\code{core} and \code{peripheral) are data frames containing the authors' names in the
#' first column and their centrality values in the second column.
get.author.class.by.type = function(network = NULL,
proj.data = NULL,
type = c("network.degree", "network.eigen", "network.hierarchy", "network.betweenness",
"network.closeness", "network.pagerank", "network.eccentricity",
"commit.count", "loc.count", "mail.count", "mail.thread.count",
"issue.count", "issue.comment.count", "issue.commented.in.count",
"issue.created.count"),
issue.type = c("all", "issues", "pull.requests"),
result.limit = NULL,
restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.by.type: starting.")
type = match.arg(type)
issue.type = match.arg(issue.type)
## Get a reasonable metric name for each classification type
metric.name = switch(type,
"network.degree" = "vertex.degree",
"network.eigen" = "eigen.centrality",
"network.hierarchy" = "hierarchy",
"network.betweenness" = "betweenness.centrality",
"network.closeness" = "closeness.centrality",
"network.pagerank" = "pagerank.centrality",
"network.eccentricity" = "eccentricity",
"commit.count" = "commit.count",
"loc.count" = "loc.count",
"mail.count" = "mail.count",
"mail.thread.count" = "mail.thread.count",
"issue.count" = "issue.count",
"issue.comment.count" = "issue.comment.count",
"issue.commented.in.count" = "issue.commented.in.count",
"issue.created.count" = "issue.created.count")
if (CLASSIFICATION.TYPE.TO.CATEGORY[[type]] == "network") {
if (is.null(network)) {
logging::logerror("For network-based classifications the parameter 'network' must not be null.")
stop("For network-based classifications the parameter 'network' must not be null.")
}
## Ensure that the parameter 'network' is of type 'igraph'
verify.argument.for.parameter(network, "igraph", "get.author.class.by.type")
if (igraph::vcount(network) == 0) {
logging::logwarn("The specified network is empty. Returning an empty classification.")
return(list("core" = create.empty.data.frame(c("author.name", metric.name), c("character", "numeric")),
"peripheral" = create.empty.data.frame(c("author.name", metric.name),
c("character", "numeric"))))
}
} else {
if (is.null(proj.data)) {
logging::logerror("For count-based classifications the parameter 'proj.data' must not be null.")
stop("For count-based classifications the parameter 'proj.data' must not be null.")
}
## Ensure that the parameter 'proj.data' is of type 'ProjectData'
verify.argument.for.parameter(proj.data, "ProjectData", "get.author.class.by.type")
}
## The centrality dataframe has two columns:
## 1. a column of author names
## 2. a column for the centrality type (which is given by the parameter 'type'),
## e.g. the vertex degree when type == network.degree or the commit count when type == commit.count
centrality.dataframe = NULL
if (type == "network.degree") {
## Get vertex degrees for all authors
vertex.degree.vec = igraph::degree(network)
## Construct centrality dataframe
centrality.dataframe = data.frame(author.name = names(vertex.degree.vec),
centrality = as.vector(vertex.degree.vec))
} else if (type == "network.eigen") {
## Get eigenvectors for all authors
## The method ignores the directed parameter if the given network is undirected
eigen.centrality.vec = tryCatch(
igraph::eigen_centrality(network, directed = TRUE),
error = function(e) {
logging::logwarn(e)
logging::logwarn("As of the error above, adjust ARPACK options 'maxiter' and 'tol'...")
adjusted.options = list(maxiter = 5000, tol = 0.1)
adjusted.computation = igraph::eigen_centrality(network, directed = TRUE,
options = adjusted.options)
logging::loginfo("eigen_centrality with adjusted ARPACK options finished successfully.")
return(adjusted.computation)
}
)
eigen.centrality.vec = eigen.centrality.vec[["vector"]]
## In case no collaboration occured, all centrality values are set to 0
if (igraph::ecount(network) == 0) {
eigen.centrality.vec[] = 0
}
## Construct centrality dataframe
centrality.dataframe = data.frame(author.name = names(eigen.centrality.vec),
centrality = as.vector(eigen.centrality.vec))
} else if (type == "network.hierarchy") {
hierarchy.base.df = metrics.hierarchy(network)
hierarchy.calculated = hierarchy.base.df[["deg"]] / hierarchy.base.df[["cc"]]
## fix all authors whose clustering coefficient is '0', since their hierarchy value
## is 'Inf'. We do not get any complications here because there are no authors with
## degree == 0 and a CC > 0 (i.e., the hierarchy value would really be 0). Authors with
## a CC == NaN (degree < 2) will stay with their hierarchy value of NaN, accordingly.
## Note that this could lead to authors who should have a high hierarchy value
## being set to 0 because they interact with clusters that have no connection
## between them. In this case, we have no way of determining what the hierarchy
## value should be without looking further into the connected clusters. We
## assume that the case where hierarchy is set to 0 even though it should
## be a high value occurs rarely.
hierarchy.calculated[is.infinite(hierarchy.calculated)] = 0
## Construct centrality dataframe
centrality.dataframe = data.frame(author.name = row.names(hierarchy.base.df),
centrality = hierarchy.calculated)
} else if (type == "network.betweenness") {
betweenness.centrality.vec = igraph::betweenness(network, directed = TRUE)
## Construct centrality dataframe
centrality.dataframe = data.frame(author.name = names(betweenness.centrality.vec),
centrality = as.vector(betweenness.centrality.vec))
} else if (type == "network.closeness") {
closeness.centrality.vec = igraph::closeness(network, mode = "all")
## Construct centrality dataframe
centrality.dataframe = data.frame(author.name = names(closeness.centrality.vec),
centrality = as.vector(closeness.centrality.vec))
} else if (type == "network.pagerank") {
pagerank.centrality.vec = igraph::page_rank(network, directed = TRUE)[["vector"]]
## Construct centrality dataframe
centrality.dataframe = data.frame(author.name = names(pagerank.centrality.vec),
centrality = as.vector(pagerank.centrality.vec))
} else if (type == "network.eccentricity") {
eccentricity.vec = igraph::eccentricity(network)
## Since core developers are expected to have a lower eccentricity,
## we need to invert all non-zero values.
## All entries with value zero are isolated vertices, so the expected value is also zero.
## Thus, they should not be inverted.
indices = which(eccentricity.vec > 0)
eccentricity.vec[indices] = max(eccentricity.vec) - eccentricity.vec[indices]
## Construct centrality dataframe
centrality.dataframe = data.frame(author.name = names(eccentricity.vec),
centrality = as.vector(eccentricity.vec))
} else if (type == "commit.count") {
## Construct centrality dataframe
centrality.dataframe = get.author.commit.count(proj.data)
} else if (type == "loc.count") {
## Construct centrality dataframe
centrality.dataframe = get.author.loc.count(proj.data)
} else if (type == "mail.count") {
## Construct centrality dataframe
centrality.dataframe = get.author.mail.count(proj.data)
} else if (type == "mail.thread.count") {
## Construct centrality dataframe
centrality.dataframe = get.author.mail.thread.count(proj.data)
}else if (type == "issue.count") {
## Construct centrality dataframe
centrality.dataframe = get.author.issue.count(proj.data, issue.type)
} else if (type == "issue.comment.count") {
## Construct centrality dataframe
centrality.dataframe = get.author.issue.comment.count(proj.data, issue.type)
} else if (type == "issue.commented.in.count") {
## Construct centrality dataframe
centrality.dataframe = get.author.issues.commented.in.count(proj.data, issue.type)
} else { # type == 'issue.created.count'
## Construct centrality dataframe
centrality.dataframe = get.author.issues.created.count(proj.data, issue.type)
}
# rename the second column of the centrality data frame to the correct name with respect to the classification type
names(centrality.dataframe)[2] = metric.name
## If the parameter 'restrict.classification.to.authors' is 'NULL', no restriction is made.
## Therefore, the restriction is defined as to include all authors (i.e., there is no restriction in the next step)
if (is.null(restrict.classification.to.authors)) {
restrict.classification.to.authors = centrality.dataframe[["author.name"]]
}
## Restrict authors as given by the parameter 'restrict.classification.to.authors' before classification
centrality.dataframe = centrality.dataframe[centrality.dataframe[["author.name"]] %in%
restrict.classification.to.authors, ]
## Retrieve classification results
classification = get.author.class(centrality.dataframe, metric.name, result.limit = result.limit,
classification.category = CLASSIFICATION.TYPE.TO.CATEGORY[[type]])
## Authors who are specified in the parameter 'restrict.classification.to.authors' but were not considered in the
## classification will be appended with a value of 'NA' to the classification as peripheral authors:
## 1) Prepare a list of author names who are specified in the parameter 'restrict.classification.to.authors' but
## are not part of the classification
remaining.authors = setdiff(restrict.classification.to.authors, centrality.dataframe[["author.name"]])
## 2) Create a dataframe for those authors, all of which getting 'NA' as centrality value
remaining.authors.df = data.frame(author.name = remaining.authors, temp = rep(NA, length(remaining.authors)))
## 3) In preparition to the coming 'rbind', adjust the column names to be the same for both dataframes
names(remaining.authors.df) = c("author.name", metric.name)
## 4) Append the newly created dataframe of authors to the classification as peripheral authors
classification[["peripheral"]] = rbind(classification[["peripheral"]], remaining.authors.df)
logging::logdebug("get.author.class.by.type: finished.")
return(classification)
}
#' Classify authors into "core" and "peripheral" independently for a list of networks or \code{ProjectData} objects.
#'
#' @param network.list a list of networks, each of which containing the authors to classify (parameter is required if
#' the parameter \code{type} specifies a network-based classification metric) [default: NULL]
#' @param range.data.list a list of \code{ProjectData} and/or \code{RangeData} objects, each of which containing the
#' authors to classify (parameter is required if the parameter \code{type} specifies a
#' count-based classification metric) [default: NULL]
#'@param type a character string declaring the classification metric. The classification metric determines which
#' numerical characteristic of authors is chosen as their centrality value.
#' The parameter currently supports the following eleven options:
#' Network-based options/metrics (parameter \code{network} has to be specified):
#' - "network.degree"
#' - "network.eigen"
#' - "network.hierarchy"
#' Count-based options/metrics (parameter \code{proj.data} has to be specified):
#' - "commit.count"
#' - "loc.count"
#' - "mail.count"
#' - "mail.thread.count"
#' - "issue.count"
#' - "issue.comment.count"
#' - "issue.commented.in.count"
#' - "issue.created.count"
#' [default: "network.degree"]
#' @param issue.type which issue type to consider for count-based metrics using issues
#' (see \code{preprocess.issue.data}). One of \code{"issues"},
#' \code{"pull.requests"} or \code{"all"}. This parameter is ignored for
#' non-issue-related classification metrics. [default: "all"]
#' @param restrict.classification.to.authors a vector of author names or a list of vectors of author names.
#' When choosing the first option (i.e., when passing a vector of author
#' names), only authors that are contained within this vector are to be
#' classified. Authors that appear in the vector but are not part of the
#' classification result (i.e., they are not present in the underlying data)
#' will be added to it afterwards (with a centrality value of \code{NA}).
#' Alternatively, a list containing vectors of author names can be passed.
#' The list must have the same length as the list of networks specified in
#' the parameter \code{network.list} or the list of \code{RangeData} objects
#' specified in the parameter \code{range.data.list}, respectively. For each
#' range, the corresponding author group in the list will then be used for
#' restriction instead of using the same group for all ranges. \code{NULL}
#' means that no restriction is made. [default: NULL]
#'
#' @return a list of classification results. Each classification result is a list containing two named list members
#' \code{core} and \code{peripheral}, each of which holding the authors classified as core or peripheral,
#' respectively. Both entries in this list (\code{core} and \code{peripheral}) are dataframes containing the
#' authors' names in the first column and their centrality values in the second column.
#'
#' @seealso get.author.class.by.type
get.author.class.overview = function(network.list = NULL, range.data.list = NULL,
type = c("network.degree", "network.eigen", "network.hierarchy",
"commit.count", "loc.count", "mail.count", "mail.thread.count",
"issue.count", "issue.comment.count", "issue.commented.in.count",
"issue.created.count"),
issue.type = c("all", "issues", "pull.requests"),
restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.overview: starting.")
type = match.arg(type)
issue.type = match.arg(issue.type)
if (CLASSIFICATION.TYPE.TO.CATEGORY[[type]] == "network") {
if (is.null(network.list)) {
logging::logerror("For network-based classifications the parameter 'network.list' must not be null.")
stop("For network-based classifications the parameter 'network.list' must not be null.")
}
data.list = network.list
} else {
if (is.null(range.data.list)) {
logging::logerror("For commit-based classifications the parameter 'range.data.list' must not be null.")
stop("For commit-based classifications the parameter 'range.data.list' must not be null.")
}
data.list = range.data.list
}
## If the parameter 'restrict.classification.to.authors' is no list but simply a vector of authors, this vector is
## replicated for each range so that each range is restricted to same group of authors
if (!is.list(restrict.classification.to.authors)) {
restrict.classification.to.authors = rep(list(restrict.classification.to.authors), length(data.list))
} else if (length(restrict.classification.to.authors) != length(data.list)) {
stop("If a list is specified in the parameter 'restrict.classification.to.authors', its length must match the
length of either the parameter 'network.list' or 'range.data.list', depending on the classification type
specified in the parameter 'type'.")
}
result = mapply(data.list, restrict.classification.to.authors, SIMPLIFY = FALSE,
FUN = function(data, restrict.classification.to.authors) {
if (CLASSIFICATION.TYPE.TO.CATEGORY[[type]] == "network") {
return(get.author.class.by.type(network = data, type = type,
restrict.classification.to.authors = restrict.classification.to.authors))
} else {
return(get.author.class.by.type(proj.data = data, type = type, issue.type = issue.type,
restrict.classification.to.authors = restrict.classification.to.authors))
}
})
logging::logdebug("get.author.class.overview: finished.")
return(result)
}
#' Get the author turnover values measured as the proportion of authors in the
#' specified range classes which were not active, i.e. do not exist,
#' in the previous range classes (saturation).
#'
#' @param author.class.overview the list of author classifications
#' @param saturation the number of ranges to look in the past [default: 1]
#'
#' @return a data.frame with information about developer turnover
get.class.turnover.overview = function(author.class.overview, saturation = 1) {
logging::logdebug("get.class.turnover.overview: starting.")
if (!is.null(names(author.class.overview))) {
versions = names(author.class.overview)
} else {
versions = seq_along(author.class.overview)
}
## Set up the data.frame for the analysis results
turnover.overview = data.frame(
versions = versions,
row.names = 1,
turnover = 0,
turnover.core = 0,
turnover.peripheral = 0,
dev.count = 0,
dev.count.core = 0,
dev.count.peripheral = 0
)
## Get all active authors for each range in the different classes (and both)
devs = sapply(author.class.overview, function(author.class) {
return(c(author.class[["core"]][["author.name"]], author.class[["peripheral"]][["author.name"]]))
})
devs.core = sapply(author.class.overview, function(author.class) {
return(author.class[["core"]][["author.name"]])
})
devs.peripheral = sapply(author.class.overview, function(author.class) {
return(author.class[["peripheral"]][["author.name"]])
})
## The author turnover measured as the proportion of devs in the current version
## range which were not active in the previous range
devs.new = devs[[1]]
devs.core.new = devs.core[[1]]
devs.peripheral.new = devs.peripheral[[1]]
turnover.overview[["dev.count"]][1] = length(devs.new)
turnover.overview[["dev.count.core"]][1] = length(devs.core.new)
turnover.overview[["dev.count.peripheral"]][1] = length(devs.peripheral.new)
for (i in 2:length(author.class.overview)) {
devs.old = devs.new
devs.core.old = devs.core.new
devs.peripheral.old = devs.peripheral.new
j = 1
while (j <= saturation) {
if ((i-j) > 0) {
devs.old = igraph::union(devs.old, devs[[i-j]])
devs.core.old = igraph::union(devs.core.old, devs.core[[i-j]])
devs.peripheral.old = igraph::union(devs.peripheral.old, devs.peripheral[[i-j]])
}
j = j + 1
}
## Find the authors which are active in the current period
devs.new = devs[[i]]
devs.core.new = devs.core[[i]]
devs.peripheral.new = devs.peripheral[[i]]
## Calculate the turnover values
turnover.overview[["turnover"]][i] = sum(!(devs.new %in% devs.old)) / length(devs.new)
turnover.overview[["turnover.core"]][i] = sum(!(devs.core.new %in% devs.core.old)) / length(devs.core.new)
turnover.overview[["turnover.peripheral"]][i] = sum(!(devs.peripheral.new %in% devs.peripheral.old)) /
length(devs.peripheral.new)
turnover.overview[["dev.count"]][i] = length(devs.new)
turnover.overview[["dev.count.core"]][i] = length(devs.core.new)
turnover.overview[["dev.count.peripheral"]][i] = length(devs.peripheral.new)
}
logging::logdebug("get.class.turnover.overview: finished.")
return(turnover.overview)
}
#' Gets a data frame to show the proportion of
#' the authors which are either only active in the current range but not in the previous ones (new) or
#' which are only active in the previous ranges (as specified in saturation) but not in the current one (gone) in
#' relation to all authors of the current and the previous ranges.
#'
#' @param author.class.overview the list of author classifications
#' @param saturation the number of ranges to look in the past [default: 1]
#'
#' @return a data.frame with information about author stability
get.unstable.authors.overview = function(author.class.overview, saturation = 1) {
logging::logdebug("get.unstable.authors.overview: starting.")
if (!is.null(names(author.class.overview))) {
versions = names(author.class.overview)
} else {
versions = seq_along(author.class.overview)
}
## Set up the data.frame for the analysis results
turnover.overview = data.frame(
versions = versions,
row.names = 1,
unstable = 0,
unstable.core = 0,
unstable.peripheral = 0
)
## Get all active authors for each range in the different classes (and both)
devs = sapply(author.class.overview, function(author.class) {
return(c(author.class[["core"]][["author.name"]], author.class[["peripheral"]][["author.name"]]))
})
devs.core = sapply(author.class.overview, function(author.class) {
return(author.class[["core"]][["author.name"]])
})
devs.peripheral = sapply(author.class.overview, function(author.class) {
return(author.class[["peripheral"]][["author.name"]])
})
devs.current = devs[[1]]
devs.core.current = devs.core[[1]]
devs.peripheral.current = devs.peripheral[[1]]
for (i in 2:length(author.class.overview)) {
devs.prev = devs.current
devs.core.prev = devs.core.current
devs.peripheral.prev = devs.peripheral.current
j = 1
while (j <= saturation) {
if ((i-j) > 0) {
devs.prev = igraph::union(devs.prev, devs[[i-j]])
devs.core.prev = igraph::union(devs.core.prev, devs.core[[i-j]])
devs.peripheral.prev = igraph::union(devs.peripheral.prev, devs.peripheral[[i-j]])
}
j = j + 1
}
## Find the authors which are active in the current period
devs.current = devs[[i]]
devs.core.current = devs.core[[i]]
devs.peripheral.current = devs.peripheral[[i]]
## Find the union of the devs which are active in the current period and in the prev periods
devs.union = igraph::union(devs.current, devs.prev)
devs.core.union = igraph::union(devs.core.current, devs.core.prev)
devs.peripheral.union = igraph::union(devs.peripheral.current, devs.peripheral.prev)
## Find the devs which are only active in the current range but not in the previous ones
devs.new = sum(!(devs.current %in% devs.prev))
devs.core.new = sum(!(devs.core.current %in% devs.core.prev))
devs.peripheral.new = sum(!(devs.peripheral.current %in% devs.peripheral.prev))
## Find the devs which are only active in the previous ranges but not in the current one
devs.gone = sum(!(devs.prev %in% devs.current))
devs.core.gone = sum(!(devs.core.prev %in% devs.core.current))
devs.peripheral.gone = sum(!(devs.peripheral.prev %in% devs.peripheral.current))
## Calculate the ratio values
turnover.overview[["unstable"]][i] = (devs.new + devs.gone) / length(devs.union)
turnover.overview[["unstable.core"]][i] = (devs.core.new + devs.core.gone) / length(devs.core.union)
turnover.overview[["unstable.peripheral"]][i] =
(devs.peripheral.new + devs.peripheral.gone) / length(devs.peripheral.union)
}
logging::logdebug("get.unstable.authors.overview: finished.")
return(turnover.overview)
}
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Network-based classification --------------------------------------------
## * Degree-based classification -------------------------------------------
#' Classify authors into "core" and "peripheral" based on the vertex degree of author vertices in the network and
#' return the classification result.
#'
#' The details of the classification algorithm is explained in the documentation of \code{get.author.class.by.type}.
#'
#' @param network the network containing the authors to classify
#' @param result.limit the maximum number of authors contained in the classification result. Only the top
#' \code{result.limit} authors of the classification stack will be contained within the returned
#' classification result. \code{NULL} means that all authors will be returned. [default: NULL]
#' @param restrict.classification.to.authors a vector of author names. Only authors that are contained within this
#' vector are to be classified. Authors that appear in the vector but are not
#' part of the classification result (i.e., they are not present in the
#' underlying data) will be added to it afterwards (with a centrality value
#' of \code{NA}). \code{NULL} means that no restriction is made.
#' [default: NULL]
#'
#' @return the classification result, that is, a list containing two named list members \code{core} and
#' \code{peripheral}, each of which holding the authors classified as core or peripheral, respectively. Both
#' entries in this list (\code{core} and \code{peripheral) are dataframes containing the authors' names in the
#' first column and their centrality values in the second column.
#'
#' @seealso get.author.class.by.type
get.author.class.network.degree = function(network, result.limit = NULL, restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.network.degree: starting.")
result = get.author.class.by.type(network = network, type = "network.degree", result.limit = result.limit,
restrict.classification.to.authors = restrict.classification.to.authors)
logging::logdebug("get.author.class.network.degree: finished.")
return(result)
}
## * Eigenvector-based classification --------------------------------------
#' Classify authors into "core" and "peripheral" based on the eigenvector-centrality of author vertices in the network
#' and return the classification result.
#'
#' The details of the classification algorithm is explained in the documentation of \code{get.author.class.by.type}.
#'
#' @param network the network containing the authors to classify
#' @param result.limit the maximum number of authors contained in the classification result. Only the top
#' \code{result.limit} authors of the classification stack will be contained within the returned
#' classification result. \code{NULL} means that all authors will be returned. [default: NULL]
#' @param restrict.classification.to.authors a vector of author names. Only authors that are contained within this
#' vector are to be classified. Authors that appear in the vector but are not
#' part of the classification result (i.e., they are not present in the
#' underlying data) will be added to it afterwards (with a centrality value
#' of \code{NA}). \code{NULL} means that no restriction is made.
#' [default: NULL]
#'
#' @return the classification result, that is, a list containing two named list members \code{core} and
#' \code{peripheral}, each of which holding the authors classified as core or peripheral, respectively. Both
#' entries in this list (\code{core} and \code{peripheral) are dataframes containing the authors' names in the
#' first column and their centrality values in the second column.
#'
#' @seealso get.author.class.by.type
get.author.class.network.eigen = function(network, result.limit = NULL, restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.network.eigen: starting.")
result = get.author.class.by.type(network = network, type = "network.eigen", result.limit = result.limit,
restrict.classification.to.authors = restrict.classification.to.authors)
logging::logdebug("get.author.class.network.eigen: finished.")
return(result)
}
## * Hierarchy-based classification ----------------------------------------
#' Classify authors into "core" and "peripheral" based on the hierarchy value of author vertices in the network and
#' return the classification result.
#'
#' The details of the classification algorithm is explained in the documentation of \code{get.author.class.by.type}.
#'
#' @param network the network containing the authors to classify
#' @param result.limit the maximum number of authors contained in the classification result. Only the top
#' \code{result.limit} authors of the classification stack will be contained within the returned
#' classification result. \code{NULL} means that all authors will be returned. [default: NULL]
#' @param restrict.classification.to.authors a vector of author names. Only authors that are contained within this
#' vector are to be classified. Authors that appear in the vector but are not
#' part of the classification result (i.e., they are not present in the
#' underlying data) will be added to it afterwards (with a centrality value
#' of \code{NA}). \code{NULL} means that no restriction is made.
#' [default: NULL]
#'
#' @return the classification result, that is, a list containing two named list members \code{core} and
#' \code{peripheral}, each of which holding the authors classified as core or peripheral, respectively. Both
#' entries in this list (\code{core} and \code{peripheral) are dataframes containing the authors' names in the
#' first column and their centrality values in the second column.
#'
#' @seealso get.author.class.by.type
get.author.class.network.hierarchy = function(network, result.limit = NULL,
restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.network.hierarchy: starting.")
result = get.author.class.by.type(network = network, type = "network.hierarchy", result.limit = result.limit,
restrict.classification.to.authors = restrict.classification.to.authors)
logging::logdebug("get.author.class.network.hierarchy: finished.")
return(result)
}
## * Betweenness-based classification --------------------------------------
#' Classify authors into "core" and "peripheral" based on the betweenness-centrality of author vertices in the network
#' and return the classification result.
#'
#' The details of the classification algorithms are explained in the documentation of \code{get.author.class.by.type}.
#'
#' @param network the network containing the authors to classify
#' @param result.limit the maximum number of authors contained in the classification result. Only the top
#' \code{result.limit} authors of the classification stack will be contained within the returned
#' classification result. \code{NULL} means that all authors will be returned. [default: NULL]
#' @param restrict.classification.to.authors a vector of author names. Only authors that are contained within this
#' vector are to be classified. Authors that appear in the vector but are not
#' part of the classification result (i.e., they are not present in the
#' underlying data) will be added to it afterwards (with a centrality value
#' of \code{NA}). \code{NULL} means that no restriction is made.
#' [default: NULL]
#'
#' @return the classification result, that is, a list containing two named list members \code{core} and
#' \code{peripheral}, each of which holding the authors classified as core or peripheral, respectively. Both
#' entries in this list (\code{core} and \code{peripheral) are dataframes containing the authors' names in the
#' first column and their centrality values in the second column.
#'
#' @seealso get.author.class.by.type
get.author.class.network.betweenness = function(network, result.limit = NULL,
restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.network.betweenness: starting.")
result = get.author.class.by.type(network = network, type = "network.betweenness", result.limit = result.limit,
restrict.classification.to.authors = restrict.classification.to.authors)
logging::logdebug("get.author.class.network.betweenness: finished.")
return(result)
}
## * Closeness-based classification --------------------------------------
#' Classify authors into "core" and "peripheral" based on the closeness-centrality of author vertices in the network
#' and return the classification result.
#'
#' The details of the classification algorithms are explained in the documentation of \code{get.author.class.by.type}.
#'
#' @param network the network containing the authors to classify
#' @param result.limit the maximum number of authors contained in the classification result. Only the top
#' \code{result.limit} authors of the classification stack will be contained within the returned
#' classification result. \code{NULL} means that all authors will be returned. [default: NULL]
#' @param restrict.classification.to.authors a vector of author names. Only authors that are contained within this
#' vector are to be classified. Authors that appear in the vector but are not
#' part of the classification result (i.e., they are not present in the
#' underlying data) will be added to it afterwards (with a centrality value
#' of \code{NA}). \code{NULL} means that no restriction is made.
#' [default: NULL]
#'
#' @return the classification result, that is, a list containing two named list members \code{core} and
#' \code{peripheral}, each of which holding the authors classified as core or peripheral, respectively. Both
#' entries in this list (\code{core} and \code{peripheral) are dataframes containing the authors' names in the
#' first column and their centrality values in the second column.
#'
#' @seealso get.author.class.by.type
get.author.class.network.closeness = function(network, result.limit = NULL,
restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.network.closeness: starting.")
result = get.author.class.by.type(network = network, type = "network.closeness", result.limit = result.limit,
restrict.classification.to.authors = restrict.classification.to.authors)
logging::logdebug("get.author.class.network.closeness: finished.")
return(result)
}
## * Pagerank-based classification --------------------------------------
#' Classify authors into "core" and "peripheral" based on the pagerank-centrality of author vertices in the network
#' and return the classification result.
#'
#' The details of the classification algorithms are explained in the documentation of \code{get.author.class.by.type}.
#'
#' @param network the network containing the authors to classify
#' @param result.limit the maximum number of authors contained in the classification result. Only the top
#' \code{result.limit} authors of the classification stack will be contained within the returned
#' classification result. \code{NULL} means that all authors will be returned. [default: NULL]
#' @param restrict.classification.to.authors a vector of author names. Only authors that are contained within this
#' vector are to be classified. Authors that appear in the vector but are not
#' part of the classification result (i.e., they are not present in the
#' underlying data) will be added to it afterwards (with a centrality value
#' of \code{NA}). \code{NULL} means that no restriction is made.
#' [default: NULL]
#'
#' @return the classification result, that is, a list containing two named list members \code{core} and
#' \code{peripheral}, each of which holding the authors classified as core or peripheral, respectively. Both
#' entries in this list (\code{core} and \code{peripheral) are dataframes containing the authors' names in the
#' first column and their centrality values in the second column.
#'
#' @seealso get.author.class.by.type
get.author.class.network.pagerank = function(network, result.limit = NULL,
restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.network.pagerank: starting.")
result = get.author.class.by.type(network = network, type = "network.pagerank", result.limit = result.limit,
restrict.classification.to.authors = restrict.classification.to.authors)
logging::logdebug("get.author.class.network.pagerank: finished.")
return(result)
}
## * Eccentricity-based classification --------------------------------------
#' Classify authors into "core" and "peripheral" based on the eccentricity of author vertices in the network
#' and return the classification result.
#'
#' The details of the classification algorithms are explained in the documentation of \code{get.author.class.by.type}.
#'
#' @param network the network containing the authors to classify
#' @param result.limit the maximum number of authors contained in the classification result. Only the top
#' \code{result.limit} authors of the classification stack will be contained within the returned
#' classification result. \code{NULL} means that all authors will be returned. [default: NULL]
#' @param restrict.classification.to.authors a vector of author names. Only authors that are contained within this
#' vector are to be classified. Authors that appear in the vector but are not
#' part of the classification result (i.e., they are not present in the
#' underlying data) will be added to it afterwards (with a centrality value
#' of \code{NA}). \code{NULL} means that no restriction is made.
#' [default: NULL]
#'
#' @return the classification result, that is, a list containing two named list members \code{core} and
#' \code{peripheral}, each of which holding the authors classified as core or peripheral, respectively. Both
#' entries in this list (\code{core} and \code{peripheral) are dataframes containing the authors' names in the
#' first column and their eccentricity values in the second column.
#'
#' @seealso get.author.class.by.type
get.author.class.network.eccentricity = function(network, result.limit = NULL,
restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.network.eccentricity: starting.")
result = get.author.class.by.type(network = network, type = "network.eccentricity", result.limit = result.limit,
restrict.classification.to.authors = restrict.classification.to.authors)
logging::logdebug("get.author.class.network.eccentricity: finished.")
return(result)
}
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Count-based classification ---------------------------------------------
## * Commit-based classification --------------------------------------------
#' Classify authors into "core" and "peripheral" based on authors' commit-counts and return the classification result.
#'
#' The details of the classification algorithm are explained in the documentation of \code{get.author.class.by.type}.
#'
#' @param proj.data the \code{ProjectData} containing the authors' commit data
#' @param result.limit the maximum number of authors contained in the classification result. Only the top
#' \code{result.limit} authors of the classification stack will be contained within the returned
#' classification result. \code{NULL} means that all authors will be returned. [default: NULL]
#' @param restrict.classification.to.authors a vector of author names. Only authors that are contained within this
#' vector are to be classified. Authors that appear in the vector but are not
#' part of the classification result (i.e., they are not present in the
#' underlying data) will be added to it afterwards (with a centrality value
#' of \code{NA}). \code{NULL} means that no restriction is made.
#' [default: NULL]
#'
#' @return the classification result, that is, a list containing two named list members \code{core} and
#' \code{peripheral}, each of which holding the authors classified as core or peripheral, respectively. Both
#' entries in this list (\code{core} and \code{peripheral) are dataframes containing the authors' names in the
#' first column and their centrality values in the second column.
#'
#' @seealso get.author.class.by.type
get.author.class.commit.count = function(proj.data, result.limit = NULL, restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.commit.count: starting.")
result = get.author.class.by.type(proj.data = proj.data, type = "commit.count", result.limit = result.limit,
restrict.classification.to.authors = restrict.classification.to.authors)
logging::logdebug("get.author.class.commit.count: finished.")
return(result)
}
## * LOC-based classification ----------------------------------------------
#' Classify authors into "core" and "peripheral" based on authors' lines of code (LOC) and return the classification
#' result. LOC are calculated independently for each author by looking at the sum of added and deleted lines of code in
#' all their commits.
#'
#' The details of the classification algorithm is explained in the documentation of \code{get.author.class.by.type}.
#'
#' @param proj.data the \code{ProjectData} containing the authors' commit data
#' @param result.limit the maximum number of authors contained in the classification result. Only the top
#' \code{result.limit} authors of the classification stack will be contained within the returned
#' classification result. \code{NULL} means that all authors will be returned. [default: NULL]
#' @param restrict.classification.to.authors a vector of author names. Only authors that are contained within this
#' vector are to be classified. Authors that appear in the vector but are not
#' part of the classification result (i.e., they are not present in the
#' underlying data) will be added to it afterwards (with a centrality value
#' of \code{NA}). \code{NULL} means that no restriction is made.
#' [default: NULL]
#'
#' @return the classification result, that is, a list containing two named list members \code{core} and
#' \code{peripheral}, each of which holding the authors classified as core or peripheral, respectively. Both
#' entries in this list (\code{core} and \code{peripheral) are dataframes containing the authors' names in the
#' first column and their centrality values in the second column.
#'
#' @seealso get.author.class.by.type
get.author.class.loc.count = function(proj.data, result.limit = NULL, restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.loc.count: starting.")
result = get.author.class.by.type(proj.data = proj.data, type = "loc.count", result.limit = result.limit,
restrict.classification.to.authors = restrict.classification.to.authors)
logging::logdebug("get.author.class.loc.count: finished.")
return(result)
}
## * Mail-based classification --------------------------------------------
#' Classify authors into "core" and "peripheral" based on authors' mail-counts and return the classification result.
#'
#' The details of the classification algorithm are explained in the documentation of \code{get.author.class.by.type}.
#'
#' @param proj.data the \code{ProjectData} containing the authors' mail data
#' @param result.limit the maximum number of authors contained in the classification result. Only the top
#' \code{result.limit} authors of the classification stack will be contained within the returned
#' classification result. \code{NULL} means that all authors will be returned. [default: NULL]
#' @param restrict.classification.to.authors a vector of author names. Only authors that are contained within this
#' vector are to be classified. Authors that appear in the vector but are not
#' part of the classification result (i.e., they are not present in the
#' underlying data) will be added to it afterwards (with a centrality value
#' of \code{NA}). \code{NULL} means that no restriction is made.
#' [default: NULL]
#'
#' @return the classification result, that is, a list containing two named list members \code{core} and
#' \code{peripheral}, each of which holding the authors classified as core or peripheral, respectively. Both
#' entries in this list (\code{core} and \code{peripheral) are dataframes containing the authors' names in the
#' first column and their centrality values in the second column.
#'
#' @seealso get.author.class.by.type
get.author.class.mail.count = function(proj.data, result.limit = NULL, restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.mail.count: starting.")
result = get.author.class.by.type(proj.data = proj.data, type = "mail.count", result.limit = result.limit,
restrict.classification.to.authors = restrict.classification.to.authors)
logging::logdebug("get.author.class.mail.count: finished.")
return(result)
}
#' Classify authors into "core" and "peripheral" based on authors' mail-thread counts and return the
#' classification result.
#'
#' The details of the classification algorithm are explained in the documentation of \code{get.author.class.by.type}.
#'
#' @param proj.data the \code{ProjectData} containing the authors' mail data
#' @param result.limit the maximum number of authors contained in the classification result. Only the top
#' \code{result.limit} authors of the classification stack will be contained within the returned
#' classification result. \code{NULL} means that all authors will be returned. [default: NULL]
#' @param restrict.classification.to.authors a vector of author names. Only authors that are contained within this
#' vector are to be classified. Authors that appear in the vector but are not
#' part of the classification result (i.e., they are not present in the
#' underlying data) will be added to it afterwards (with a centrality value
#' of \code{NA}). \code{NULL} means that no restriction is made.
#' [default: NULL]
#'
#' @return the classification result, that is, a list containing two named list members \code{core} and
#' \code{peripheral}, each of which holding the authors classified as core or peripheral, respectively. Both
#' entries in this list (\code{core} and \code{peripheral) are dataframes containing the authors' names in the
#' first column and their centrality values in the second column.
#'
#' @seealso get.author.class.by.type
get.author.class.mail.thread.count = function(proj.data, result.limit = NULL,
restrict.classification.to.authors = NULL) {
logging::logdebug("get.author.class.mail.thread.count: starting.")
result = get.author.class.by.type(proj.data = proj.data, type = "mail.thread.count", result.limit = result.limit,
restrict.classification.to.authors = restrict.classification.to.authors)
logging::logdebug("get.author.class.mail.thread.count: finished.")
return(result)
}
## * Issue-based classification --------------------------------------------
#' Classify authors into "core" and "peripheral" based on authors' issue-counts and return the classification result.
#'
#' The details of the classification algorithm are explained in the documentation of \code{get.author.class.by.type}.
#'