diff --git a/tests/testthat/test-blobPlotStabilityAcrossTasks.R b/tests/testthat/test-blobPlotStabilityAcrossTasks.R
index ffadb9f..6b90733 100644
--- a/tests/testthat/test-blobPlotStabilityAcrossTasks.R
+++ b/tests/testthat/test-blobPlotStabilityAcrossTasks.R
@@ -1,46 +1,78 @@
 test_that("blob plot for visualizing ranking stability across tasks 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")
 
   expect_error(stability(ranking),
                "The stability of rankings across tasks cannot be computed for less than two tasks.", fixed=TRUE)
 })
 
 test_that("blob plot for visualizing ranking stability across tasks 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")
 
   actualPlot <- stability(ranking)
   expect_is(actualPlot, "ggplot")
 })
+
+test_that("blob plot for visualizing ranking stability across tasks returns one plot for multi-task data set when consensus ranking is given", {
+  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")
+
+  meanRanks <- ranking%>%consensus(method = "euclidean")
+
+  actualPlot <- stability(ranking, ordering = names(meanRanks))
+  expect_is(actualPlot, "ggplot")
+})