diff --git a/.RData b/.RData
index 4033736f9d99fd6e5d26db52aa9d6a8a595d719a..eb35a777cff0701a41006d04fde53f942093c6d0 100644
Binary files a/.RData and b/.RData differ
diff --git a/.Rhistory b/.Rhistory
new file mode 100644
index 0000000000000000000000000000000000000000..05c3e466ee3610964a17068071be0393ae032251
--- /dev/null
+++ b/.Rhistory
@@ -0,0 +1,132 @@
+load("E:\\Documents\\IUT - Semestre 4\\Module - P4a - Performance\\P4a\\.RData")
+perfArray
+GraphArray <- ggplot(perfArray, aes(x=Taille, y=CPU, color="red")) + geom_point()
+library(ggplot2)
+GraphArray <- ggplot(perfArray, aes(x=Taille, y=CPU, color="red")) + geom_point()
+GraphArray
+perfTableau
+GraphTableau <- ggplot(perfTableau, aes(x=Taille, y=CPU, color="blue")) + geom_point()
+GraphTableau
+GraphTableau
+GraphTableau <- ggplot(perfTableau, aes(x=Taille, y=CPU, color="green")) + geom_point()
+GraphTableau
+GraphTableau <- ggplot(perfTableau, aes(x=Taille, y=CPU, color='green')) + geom_point()
+GraphTableau
+GraphArray
+GraphTableau <- ggplot(perfTableau, aes(x=Taille, y=CPU, color='green')) + geom_point(color="green")
+GraphTableau
+GraphArray <- ggplot(perfArray, aes(x=Taille, y=CPU, color="red")) + geom_point(color="red")
+GraphArray
+perfLinked
+GraphLinked = ggplot(perfLinked, aes(x=Taille, y=CPU, color="blue")) + geom_point(color="blue")
+GraphLinked
+GraphPerfInitializing <- rbind(GraphArray, GraphTableau, GraphLinked)
+GraphPerfInitializing
+GraphPerf <- ggplot(GraphPerfInitializing, aes(x=Taille, y=CPU)) + geom_point()
+ggplot(GraphPerfInitializing, aes(x=Taille, y=CPU)) + geom_point()
+GraphTableau
+test<- rbind(perfArray, perfTableau, perfLinked)
+GraphTest <- ggplot(test, aes(x=Taille, y=CPU)) + geom_point()
+GraphTest
+ggplot(perfArray, aes(x=Taille, y=CPU)) + geom_point() + geom_smooth(fill="blue", colour="darkblue", size=1)
+warnings()
+save.image("E:\\Documents\\IUT - Semestre 4\\Module - P4a - Performance\\P4a\\.RData")
+q()
+load("E:\\Documents\\IUT - Semestre 4\\Module - P4a - Performance\\P4a\\.RData")
+perfArray
+Array
+graphArray
+arrayGraph
+GraphArray
+GraphArray <- ggplot(perfArray, aes(x=Taille, y=CPU, color="red")) + geom_point(color="red") + geom_smooth()
+library(ggplot2)
+GraphArray <- ggplot(perfArray, aes(x=Taille, y=CPU, color="red")) + geom_point(color="red") + geom_smooth()
+GraphArray <- ggplot(perfArray, aes(x=Taille, y=CPU, color="red")) + geom_point(color="red") + geom_smooth()
+GraphArray
+warnings()
+q()
+perf <- read.csv2("perf.csv", sep="\t", dec=".")
+perfTableau <- read.csv2("perfTableau.csv", sep="\t", dec=".")
+perfTableau <- read.csv2("perfTableau.csv", sep="\t", dec=".")
+perfArray <- read.csv2("perfArray.csv", sep="\t", dec=".")
+perfLinked <- read.csv2("perfLinked.csv", sep="\t", dec=".")
+graphTableau <- ggplot(perfTableau, aes(x=Taille,y=CPU,label="Performance Add Tableau")) + geom_smooth(color=blue")
+)
+"
+graphTableau <- ggplot(perfTableau, aes(x=Taille,y=CPU,label="Performance Add Tableau")) + geom_smooth(color="blue")
+library(ggplot2)
+graphTableau <- ggplot(perfTableau, aes(x=Taille,y=CPU,label="Performance Add Tableau")) + geom_smooth(color="blue")
+graphTableau
+graphTableau <- ggplot(perfTableau, aes(x=Taille,y=CPU,label="Performance Add Tableau")) + geom_smooth(color="blue") + ggtitle("Evaluation du temps d'éxécution de Add sur un tableau")
+graphTableau <- ggplot(perfTableau, aes(x=Taille,y=CPU,colour="Performance Add Tableau")) + geom_smooth(color="blue") + ggtitle("Evaluation du temps d'éxécution de Add sur un tableau")
+graphTableau
+graphTableau <- ggplot(perfTableau, aes(x=Taille,y=CPU,colour=Performance Add Tableau)) + geom_smooth(color="blue") + ggtitle("Evaluation du temps d'éxécution de Add sur un tableau")
+graphTableau <- ggplot(perfTableau, aes(x=Taille,y=CPU,color="Performance Add Tableau")) + geom_smooth(color="blue") + ggtitle("Evaluation du temps d'éxécution de Add sur un tableau")
+graphTableau
+graphTableau <- ggplot(perfTableau, aes(x=Taille,y=CPU,color="red")) + geom_smooth(color="blue") + ggtitle("Evaluation du temps d'éxécution de Add sur un tableau")
+graphTableau
+graphTableau <- ggplot(perfTableau, aes(x=Taille,y=CPU,color=red)) + geom_smooth(color="blue") + ggtitle("Evaluation du temps d'éxécution de Add sur un tableau")
+graphTableau
+graphTableau <- ggplot(perfTableau, aes(x=Taille,y=CPU,color=time)) + geom_smooth(color="blue") + ggtitle("Evaluation du temps d'éxécution de Add sur un tableau")
+graphTableau
+graphTableau
+graphTableau <- ggplot(perfTableau, aes(x=Taille,y=CPU,color=CPU)) + geom_smooth(color="blue") + ggtitle("Evaluation du temps d'éxécution de Add sur un tableau")
+graphTableau
+graphArray <- ggplot(perfArray, aes(x=Taille,y=CPU,color=CPU)) + geom_smooth(color="red") + ggtitle("Evaluation du temps d'éxécution de Add sur une Array")
+graphArray
+graphLinked <- ggplot(perfLinked, aes(x=Taille,y=CPU,color=CPU)) + geom_smooth(color="green") + ggtitle("Evaluation du temps d'éxécution de Add sur une LinkedList")
+graphLinked
+graph <- ggarrange(graphTableau, graphArray, graphLinked, Labels=c("Tableau","Array","Linked"), ncol=2, nrow=2)
+library(ggpubr)
+install.package(ggpubr)
+instal.package(ggpubr)
+local({pkg <- select.list(sort(.packages(all.available = TRUE)),graphics=TRUE)
+if(nchar(pkg)) library(pkg, character.only=TRUE)})
+instal.packages(ggpubr)
+install.packages(ggpubr)
+if(!require(devtools)) install.packages("devtools")
+devtools::install_github("kassambara/ggpubr")
+devtools::install_github("kassambara/ggpubr")
+install.packages(ggpubr)
+local({pkg <- select.list(sort(.packages(all.available = TRUE)),graphics=TRUE)
+if(nchar(pkg)) library(pkg, character.only=TRUE)})
+library(devtools)
+library("devtools")
+library("devtools")
+local({pkg <- select.list(sort(.packages(all.available = TRUE)),graphics=TRUE)
+if(nchar(pkg)) library(pkg, character.only=TRUE)})
+local({pkg <- select.list(sort(.packages(all.available = TRUE)),graphics=TRUE)
+if(nchar(pkg)) library(pkg, character.only=TRUE)})
+print(perfArray)
+print(graphArray)
+print(graphLinked)
+using pushViewport()
+pushViewport(viewport(layout = grid.layout(2,2)))
+if(!require(devtools)) install.packages("devtools")
+devtools::install_github("kassambara/ggpubr")
+devtools::install_github("kassambara/ggpubr")
+install.packages("ggpubr")
+libraryr(ggpubr)
+library(ggpubr)
+graph <- ggarrange(graphTableau, graphArray, graphLinked, Labels=c("Tableau","Array","Linked"), ncol=2, nrow=2)
+graph
+save.image("E:\\Documents\\IUT - Semestre 4\\Module - P4a - Performance\\P4a\\.RData")
+perf <- read.csv2("perf.csv", sep="\t", dec=".")
+perf
+perf <- read.csv2("perf.csv", sep="\t", dec=".")
+perf
+ggplot(perf,aes(y = CPU, x = Taille, colour = Structure, shape =Structure))
+ggplot(perf,aes(y = CPU, x = Taille, colour = Structure, shape =Structure)) + geom_point() + geom_smooth()
+ggplot(perf,aes(y = CPU, x = Taille, colour = Structure, shape =Structure)) + geom_point() + geom_smooth() + ggtitle("Evaluation de la performance CPU de la méthode ADD en fonction de la taille")
+perfAdd <- read.csv2("perf.csv", sep="\t", dec=".")
+GraphAdd <- ggplot(perfAdd,aes(y = CPU, x = Taille, colour = Structure, shape =Structure)) + geom_point() + geom_smooth() + ggtitle("Evaluation de la performance CPU de la méthode ADD en fonction de la taille")
+GraphAdd
+GraphAddCPU <- ggplot(perfAdd,aes(y = CPU, x = Taille, colour = Structure, shape =Structure)) + geom_point() + geom_smooth() + ggtitle("Evaluation de la performance CPU de la méthode ADD en fonction de la taille")
+GraphAddMemoire <- ggplot(perfAdd,aes(y = CPU, x = Taille, colour = Structure, shape =Structure)) + geom_point() + geom_smooth() + ggtitle("Evaluation de la performance mémoire de la méthode ADD en fonction de la taille")
+GraphAddMemoire
+GraphAddMemoire <- ggplot(perfAdd,aes(y = Memoire, x = Taille, colour = Structure, shape =Structure)) + geom_point() + geom_smooth() + ggtitle("Evaluation de la performance mémoire de la méthode ADD en fonction de la taille")
+GraphAddMemoire
+GraphAddMemoire <- ggplot(perfAdd,aes(y = Mem, x = Taille, colour = Structure, shape =Structure)) + geom_point() + geom_smooth() + ggtitle("Evaluation de la performance mémoire de la méthode ADD en fonction de la taille")
+GraphAddMemoire
+save.image("E:\\Documents\\IUT - Semestre 4\\Module - P4a - Performance\\P4a\\.RData")
+q()
diff --git a/Main.jar b/Main.jar
new file mode 100644
index 0000000000000000000000000000000000000000..63e58a1463c803785b52cd04de9e68f8e674af45
Binary files /dev/null and b/Main.jar differ
diff --git a/README.md b/README.md
index 2ba987f388fed27e3d8191461b31ada74f7c37ad..6e06052f8de74c8c9cdcba17fc96ea340e0f6488 100644
--- a/README.md
+++ b/README.md
@@ -26,7 +26,7 @@ Contrainte : il faut de l’abstraction, et au moins un tableau ET une liste cha
 
 **Organisation du projet**
 
-![](/UML_P4a.PNG)
+![](/UML_P4a.png)
 
 ## Dispositif expérimental
 
diff --git a/UML.drawio b/UML.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..3014211c3dbdfc22837941f9d70552f3e1a93fc9
--- /dev/null
+++ b/UML.drawio
@@ -0,0 +1 @@
+<mxfile host="app.diagrams.net" modified="2021-03-09T14:27:15.252Z" agent="5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.190 Safari/537.36" etag="9Kk41M0dOw_UMj_LRa3x" version="14.4.6" type="device"><diagram id="R8-pWj9DbBJSb4AKZ-qf" name="Page-1">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</diagram></mxfile>
\ No newline at end of file
diff --git a/UML_P4a.png b/UML_P4a.png
new file mode 100644
index 0000000000000000000000000000000000000000..e3044cf2999560b4589f74ddd31e32b603b932a9
Binary files /dev/null and b/UML_P4a.png differ
diff --git a/perf.csv b/perf.csv
index a846d30d720ffa201d9768f3ea57a66230f5e36e..4f86beb8c374edb44b28dab7ebed37a98e2c603b 100644
--- a/perf.csv
+++ b/perf.csv
@@ -1,27 +1,201 @@
-Tableau
-Taille, CPU, Mem
-100, 0.03, 27576
-1000, 0.06, 27592
-10000, 0.04, 28100
-100000, 0.06, 30216
-1000000, 0.09, 48400
-10000000, 0.90, 310548
-100000000, 14.26, 2427740
-Array
-Taille, CPU, Mem
-100, 0.03, 27588
-1000, 0.03, 27632
-10000, 0.03, 28288
-100000, 0.06, 31348
-1000000, 0.14, 59088
-10000000, 1.73, 369432
-100000000, 21.17, 2843616
-Linked
-Taille, CPU, Mem
-100, 0.03, 27604
-1000, 0.04, 27676
-10000, 0.06, 28336
-100000, 0.06, 32424
-1000000, 0.40, 94300
-10000000, 4.76, 524520
-100000000, 68.70, 4308476
+Structure	Test	Taille	CPU	Mem
+Tableau	1	11797000	1.31	343900
+Tableau	2	8166000	0.70	233216
+Tableau	3	13790000	1.65	356292
+Tableau	4	11637000	1.34	345296
+Tableau	5	23771000	2.37	689876
+Tableau	6	11941000	1.76	344132
+Tableau	7	713000	0.12	42612
+Tableau	8	4318000	0.40	119112
+Tableau	9	32655000	4.23	875968
+Tableau	10	13708000	1.45	356808
+Tableau	11	14941000	1.68	359964
+Tableau	12	15392000	1.73	389132
+Tableau	13	759000	0.07	43416
+Tableau	14	23614000	2.40	689056
+Tableau	15	10158000	0.85	302888
+Tableau	16	16554000	1.93	445944
+Tableau	17	28786000	2.92	781460
+Tableau	18	25403000	2.76	765952
+Tableau	19	23059000	2.23	682272
+Tableau	20	13667000	1.56	356096
+Tableau	21	7115000	0.45	212516
+Tableau	22	17501000	2.64	480652
+Tableau	23	18640000	2.78	506536
+Tableau	24	257000	0.06	33684
+Tableau	25	5044000	0.40	152680
+Tableau	26	18854000	3.06	509684
+Tableau	27	14979000	1.50	360400
+Tableau	28	9572000	0.68	300576
+Tableau	29	23717000	2.32	685720
+Tableau	30	22982000	1.89	571200
+Tableau	31	22523000	1.85	564612
+Tableau	32	10340000	0.85	303644
+Tableau	33	22902000	1.96	569696
+Tableau	34	28631000	3.04	764548
+Tableau	35	17528000	3.04	480468
+Tableau	36	20195000	3.37	538412
+Tableau	37	15525000	1.73	364192
+Tableau	38	10613000	0.78	304644
+Tableau	39	24075000	3.67	658280
+Tableau	40	14601000	1.62	357228
+Tableau	41	9476000	0.87	299252
+Tableau	42	16182000	1.45	368228
+Tableau	43	5125000	0.46	151040
+Tableau	44	31959000	4.39	844072
+Tableau	45	14531000	1.75	385024
+Tableau	46	19408000	3.14	521208
+Tableau	47	18598000	2.87	503544
+Tableau	48	14159000	1.75	353492
+Tableau	49	21235000	2.07	536552
+Tableau	50	2213000	0.20	96272
+Array	1	10315000	2.67	373540
+Array	2	1286000	0.06	70756
+Array	3	26868000	6.43	925488
+Array	4	1845000	0.26	107172
+Array	5	24806000	4.15	841244
+Array	6	9983000	2.42	371548
+Array	7	12737000	3.07	443156
+Array	8	31557000	7.17	1065296
+Array	9	31490000	7.31	1092564
+Array	10	17012000	3.43	593728
+Array	11	1472000	0.35	96484
+Array	12	7698000	1.39	282748
+Array	13	23331000	4.59	769748
+Array	14	4994000	0.98	214032
+Array	15	30203000	6.42	968600
+Array	16	27670000	4.32	842488
+Array	17	13408000	3.32	452796
+Array	18	7322000	1.51	280932
+Array	19	24355000	5.01	792152
+Array	20	6366000	1.29	280628
+Array	21	10108000	2.14	364604
+Array	22	23009000	4.15	772016
+Array	23	28412000	4.87	907132
+Array	24	5398000	1.07	220844
+Array	25	28364000	6.15	871080
+Array	26	19539000	3.51	594276
+Array	27	29098000	7.85	941600
+Array	28	22299000	6.71	780996
+Array	29	14385000	3.12	508348
+Array	30	20619000	3.43	645844
+Array	31	5565000	0.85	223180
+Array	32	22529000	3.96	769200
+Array	33	16979000	3.51	593924
+Array	34	28921000	4.68	842872
+Array	35	28208000	4.31	842084
+Array	36	12434000	2.51	383216
+Array	37	22912000	4.29	775936
+Array	38	3459000	0.50	154244
+Array	39	10163000	2.39	365056
+Array	40	30438000	6.45	970160
+Array	41	9501000	2.26	364408
+Array	42	5408000	1.01	220584
+Array	43	7486000	1.59	281012
+Array	44	31843000	7.12	1095532
+Array	45	26598000	3.73	842636
+Array	46	19435000	3.34	596964
+Array	47	15321000	3.28	520288
+Array	48	19483000	3.17	597024
+Array	49	15012000	3.00	523032
+Array	50	26206000	4.35	840600
+Linked	1	14278000	7.56	772120
+Linked	2	20522000	10.39	1042584
+Linked	3	7287000	3.95	376932
+Linked	4	696000	0.26	78316
+Linked	5	13809000	8.17	765984
+Linked	6	28957000	17.20	1428072
+Linked	7	23574000	13.75	1158152
+Linked	8	10911000	6.23	661668
+Linked	9	20974000	10.73	1055816
+Linked	10	5826000	2.71	353996
+Linked	11	12058000	6.37	662956
+Linked	12	4735000	2.32	286480
+Linked	13	29197000	16.98	1429412
+Linked	14	15900000	8.64	859316
+Linked	15	24727000	13.39	1160996
+Linked	16	24019000	12.71	1155592
+Linked	17	23074000	13.81	1139456
+Linked	18	4985000	2.07	280620
+Linked	19	11597000	6.79	641480
+Linked	20	6848000	4.62	361460
+Linked	21	20634000	11.95	1057272
+Linked	22	14742000	8.34	742256
+Linked	23	24361000	15.82	1276240
+Linked	24	10553000	6.85	637972
+Linked	25	28159000	18.71	1412356
+Linked	26	11363000	7.25	641320
+Linked	27	18275000	11.68	961572
+Linked	28	15076000	9.90	832720
+Linked	29	937000	0.53	94268
+Linked	30	25853000	16.14	1285976
+Linked	31	14963000	7.84	771352
+Linked	32	25294000	14.92	1300764
+Linked	33	23465000	12.95	1156372
+Linked	34	22955000	14.65	1137660
+Linked	35	27698000	17.06	1417600
+Linked	36	21766000	13.15	1145816
+Linked	37	4058000	2.09	281260
+Linked	38	20027000	12.03	1034644
+Linked	39	13904000	7.70	767256
+Linked	40	17898000	9.32	856000
+Linked	41	8144000	3.81	375672
+Linked	42	20864000	10.79	1053304
+Linked	43	17793000	8.96	869748
+Linked	44	17323000	9.76	863384
+Linked	45	8432000	4.73	460316
+Linked	46	10632000	5.42	547440
+Linked	47	14806000	8.32	753160
+Linked	48	6059000	2.76	347912
+Linked	49	25885000	15.87	1279276
+Linked	50	32566000	20.04	1612876
+Maillon	1	18520000	4.01	686260
+Maillon	2	2410000	1.32	117680
+Maillon	3	15546000	2.89	513916
+Maillon	4	28782000	4.42	838148
+Maillon	5	27312000	4.68	878476
+Maillon	6	29996000	5.45	994392
+Maillon	7	7387000	1.75	297592
+Maillon	8	6622000	1.42	224716
+Maillon	9	30046000	5.50	1018232
+Maillon	10	11692000	2.51	366116
+Maillon	11	4734000	1.35	212068
+Maillon	12	15072000	2.70	499292
+Maillon	13	25509000	4.48	860900
+Maillon	14	9184000	2.31	327332
+Maillon	15	21616000	4.37	742784
+Maillon	16	15828000	2.93	513940
+Maillon	17	17598000	3.59	656176
+Maillon	18	9095000	1.90	282628
+Maillon	19	12353000	2.79	381044
+Maillon	20	706000	0.15	47116
+Maillon	21	12100000	2.20	352948
+Maillon	22	19645000	3.96	638440
+Maillon	23	13976000	3.17	474072
+Maillon	24	25591000	4.51	851680
+Maillon	25	7165000	1.93	294620
+Maillon	26	1678000	0.84	98336
+Maillon	27	5326000	1.56	214796
+Maillon	28	4390000	1.10	169344
+Maillon	29	26082000	4.37	843172
+Maillon	30	2386000	0.87	108892
+Maillon	31	9896000	2.14	353832
+Maillon	32	27561000	4.42	869976
+Maillon	33	10480000	2.18	383432
+Maillon	34	25517000	4.31	863732
+Maillon	35	440000	0.04	40056
+Maillon	36	25887000	4.37	846120
+Maillon	37	31290000	5.54	1015908
+Maillon	38	8979000	1.93	289272
+Maillon	39	21349000	3.23	681984
+Maillon	40	8257000	1.84	279348
+Maillon	41	5320000	1.21	218332
+Maillon	42	26300000	4.57	851036
+Maillon	43	31038000	6.21	1020768
+Maillon	44	4385000	1.54	170384
+Maillon	45	3979000	1.31	160460
+Maillon	46	8843000	1.82	312764
+Maillon	47	931000	0.14	51924
+Maillon	48	31311000	5.70	1025596
+Maillon	49	5685000	1.28	215732
+Maillon	50	6524000	1.51	223644
diff --git a/perf.sh b/perf.sh
deleted file mode 100644
index 3cdd250e502869943d74f035d5cf01bbe088a6ac..0000000000000000000000000000000000000000
--- a/perf.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-##!/usr/bin/env bash
-
-
-NTEST=3
-TAILLES="100 1000 10000 100000 1000000 10000000 100000000"
-
-echo "Taille, CPU, Mem"
-
-for taille in $TAILLES
-do
-  for i in `seq $NTEST`
-  do
-  res=`time  "%U, %M" ./Main $taille > /dev/null`
-
-    echo "$taille, $req"
-  done
-done
diff --git a/perf2.sh b/perf2.sh
index 714fe91ee610ec8b64b11cd8a309180c0b42ca3c..24c86b9842a4bd9f79c3b8d1386c64f075300946 100644
--- a/perf2.sh
+++ b/perf2.sh
@@ -1,13 +1,40 @@
 #!/usr/bin/env bash
 
+NTEST=50
 
-TAILLE="1000000 2000000 3000000 4000000 5000000 6000000 7000000 8000000 9000000 10000000 11000000 1200000"
-NTEST=20
 
-echo "Taille, CPU, Mem"
+echo -e "Structure\tTest\tTaille\tCPU\tMem"
 for i in `seq $NTEST`
+do
+  name="Tableau"
+  taille=${RANDOM}000
+  res=`(/usr/bin/time -f "%U\t%M" java -jar Main.jar "Tableau" $taille > /dev/null) 2>&1`
+  echo -e "$name\t$i\t$taille\t$res"
+done
+
+
+for i in `seq $NTEST`
+do
+  name="Array"
+  taille=${RANDOM}000
+  res=`(/usr/bin/time -f "%U\t%M" java -jar Main.jar "Array" $taille > /dev/null) 2>&1`
+  echo -e "$name\t$i\t$taille\t$res"
+done
 
 
-  res=`(/usr/bin/time -f "%U, %M" java -jar Main.jar "Linked" $nombre > /dev/null) 2>&1`
-    echo "$taille, $res"
+for i in `seq $NTEST`
+do
+  name="Linked"
+  taille=${RANDOM}000
+  res=`(/usr/bin/time -f "%U\t%M" java -jar Main.jar "Linked" $taille > /dev/null) 2>&1`
+  echo -e "$name\t$i\t$taille\t$res"
+done
+
+
+for i in `seq $NTEST`
+do
+  name="Maillon"
+  taille=${RANDOM}000
+  res=`(/usr/bin/time -f "%U\t%M" java -jar Main.jar "Maillon" $taille > /dev/null) 2>&1`
+  echo -e "$name\t$i\t$taille\t$res"
 done