diff --git a/tests/testthat/test-blobPlotStabilityByAlgorithm.R b/tests/testthat/test-blobPlotStabilityByAlgorithm.R
index 38497cc..a41862d 100644
--- a/tests/testthat/test-blobPlotStabilityByAlgorithm.R
+++ b/tests/testthat/test-blobPlotStabilityByAlgorithm.R
@@ -1,108 +1,140 @@
 # Copyright (c) German Cancer Research Center (DKFZ)
 # All rights reserved.
 #
 # This file is part of challengeR.
 #
 # challengeR 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, either version 2 of the License, or
 # (at your option) any later version.
 #
 # challengeR 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 challengeR. If not, see <https://www.gnu.org/licenses/>.
 
 test_that("blob plot for visualizing ranking stability by algorithm raises error for single-task data set", {
   data <- rbind(
     data.frame(algo="A1", value=0.8, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"),
     data.frame(algo="A3", value=0.4, case="C1"),
     data.frame(algo="A1", value=0.2, case="C2"),
     data.frame(algo="A2", value=0.1, case="C2"),
     data.frame(algo="A3", value=0.0, case="C2"))
 
   challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=median, ties.method="min")
 
   set.seed(1)
   rankingBootstrapped <- ranking%>%bootstrap(nboot=10)
 
   expect_error(stabilityByAlgorithm(rankingBootstrapped),
                "The stability of rankings by algorithm cannot be computed for less than two tasks.", fixed=TRUE)
 })
 
 test_that("blob plot for visualizing ranking stability by algorithm returns one plot for multi-task data set", {
   dataTask1 <- cbind(task="T1",
                      rbind(
                         data.frame(algo="A1", value=0.8, case="C1"),
                         data.frame(algo="A2", value=0.6, case="C1"),
                         data.frame(algo="A3", value=0.4, case="C1"),
                         data.frame(algo="A1", value=0.2, case="C2"),
                         data.frame(algo="A2", value=0.1, case="C2"),
                         data.frame(algo="A3", value=0.0, case="C2")
                       ))
   dataTask2 <- cbind(task="T2",
                      rbind(
                        data.frame(algo="A1", value=0.2, case="C1"),
                        data.frame(algo="A2", value=0.3, case="C1"),
                        data.frame(algo="A3", value=0.4, case="C1"),
                        data.frame(algo="A1", value=0.7, case="C2"),
                        data.frame(algo="A2", value=0.8, case="C2"),
                        data.frame(algo="A3", value=0.9, case="C2")
                      ))
 
   data <- rbind(dataTask1, dataTask2)
 
   challenge <- as.challenge(data, by="task", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=median, ties.method="min")
 
   set.seed(1)
   rankingBootstrapped <- ranking%>%bootstrap(nboot=10)
 
   actualPlot <- stabilityByAlgorithm(rankingBootstrapped)
   expect_is(actualPlot, "ggplot")
 })
 
 test_that("blob plot for visualizing ranking stability by algorithm returns a plot for each algorithm", {
   dataTask1 <- cbind(task="T1",
                      rbind(
                        data.frame(algo="A1", value=0.8, case="C1"),
                        data.frame(algo="A2", value=0.6, case="C1"),
                        data.frame(algo="A3", value=0.4, case="C1"),
                        data.frame(algo="A1", value=0.2, case="C2"),
                        data.frame(algo="A2", value=0.1, case="C2"),
                        data.frame(algo="A3", value=0.0, case="C2")
                      ))
   dataTask2 <- cbind(task="T2",
                      rbind(
                        data.frame(algo="A1", value=0.2, case="C1"),
                        data.frame(algo="A2", value=0.3, case="C1"),
                        data.frame(algo="A3", value=0.4, case="C1"),
                        data.frame(algo="A1", value=0.7, case="C2"),
                        data.frame(algo="A2", value=0.8, case="C2"),
                        data.frame(algo="A3", value=0.9, case="C2")
                      ))
 
   data <- rbind(dataTask1, dataTask2)
 
   challenge <- as.challenge(data, by="task", algorithm="algo", case="case", value="value", smallBetter=FALSE)
 
   ranking <- challenge%>%aggregateThenRank(FUN=median, ties.method="min")
 
   set.seed(1)
   rankingBootstrapped <- ranking%>%bootstrap(nboot=10)
 
   meanRanks <- ranking%>%consensus(method = "euclidean")
 
   actualPlot <- stabilityByAlgorithm(rankingBootstrapped, ordering = names(meanRanks), single = TRUE)
   expect_equal(length(actualPlot), 3)
   expect_is(actualPlot[[1]], "ggplot")
   expect_is(actualPlot[[2]], "ggplot")
   expect_is(actualPlot[[3]], "ggplot")
 })
+
+test_that("blob plot for visualizing ranking stability by algorithm returns one plot if #algorithms equals #tasks", {
+  dataTask1 <- cbind(task="T1",
+                     rbind(
+                       data.frame(algo="A1", value=0.8, case="C1"),
+                       data.frame(algo="A2", value=0.6, case="C1"),
+                       data.frame(algo="A1", value=0.2, case="C2"),
+                       data.frame(algo="A2", value=0.1, case="C2")
+                     ))
+  dataTask2 <- cbind(task="T2",
+                     rbind(
+                       data.frame(algo="A1", value=0.2, case="C1"),
+                       data.frame(algo="A2", value=0.3, case="C1"),
+                       data.frame(algo="A1", value=0.7, case="C2"),
+                       data.frame(algo="A2", value=0.8, case="C2")
+                     ))
+
+  data <- rbind(dataTask1, dataTask2)
+
+  challenge <- as.challenge(data, by="task", algorithm="algo", case="case", value="value", smallBetter=FALSE)
+
+  ranking <- challenge%>%aggregateThenRank(FUN=median, ties.method="min")
+
+  set.seed(1)
+  rankingBootstrapped <- ranking%>%bootstrap(nboot=10)
+
+  meanRanks <- ranking%>%consensus(method = "euclidean")
+
+  actualPlot <- stabilityByAlgorithm(rankingBootstrapped, ordering = names(meanRanks), single = FALSE)
+  expect_is(actualPlot, "ggplot")
+})
+