rq2 results method granularity

data/a-priori-raw/apriori.R → data/a-priori/apriori-class.R

@@ -3,7 +3,7 @@ library(arules)
 tools <- c('evo', 'jtexpert', 'randoop')
 
 print('EvoSuite')
-file <- read.csv("apriori/evo.csv")
+file <- read.csv("class/evosuite.csv")
 
 for (i in 1:7) {
   file[, i] <- as.factor(file[, i])
@@ -25,7 +25,7 @@ rules.count <- sort(rules, decreasing = TRUE, na.last = NA, by = "count")
 inspect(rules.count)
 
 print('JTExpert')
-file <- read.csv("apriori/jtexpert.csv")
+file <- read.csv("class/jtexpert.csv")
 
 for (i in 1:7) {
   file[, i] <- as.factor(file[, i])
@@ -47,7 +47,7 @@ rules.count <- sort(rules, decreasing = TRUE, na.last = NA, by = "count")
 inspect(rules.count)
 
 print('Randoop')
-file <- read.csv("apriori/randoop.csv")
+file <- read.csv("class/randoop.csv")
 
 for (i in 1:7) {
   file[, i] <- as.factor(file[, i])

data/a-priori/apriori-method-results.txt

@@ -0,0 +1,115 @@
+[1] "EvoSuite"
+Apriori
+
+Parameter specification:
+ confidence minval smax arem  aval originalSupport maxtime support minlen
+       0.01    0.1    1 none FALSE            TRUE       5    0.01      2
+ maxlen target   ext
+     10  rules FALSE
+
+Algorithmic control:
+ filter tree heap memopt load sort verbose
+    0.1 TRUE TRUE  FALSE TRUE    2    TRUE
+
+Absolute minimum support count: 210
+
+set item appearances ...[5 item(s)] done [0.00s].
+set transactions ...[5 item(s), 21076 transaction(s)] done [0.01s].
+sorting and recoding items ... [4 item(s)] done [0.00s].
+creating transaction tree ... done [0.00s].
+checking subsets of size 1 2 3 done [0.00s].
+writing ... [9 rule(s)] done [0.00s].
+creating S4 object  ... done [0.00s].
+    lhs            rhs    support    confidence lift      count
+[1] {ET=1}      => {AR=1} 0.27785158 0.65110073 1.2759274 5856
+[2] {AR=1}      => {ET=1} 0.27785158 0.54449093 1.2759274 5856
+[3] {MG=1}      => {AR=1} 0.01973809 0.41893253 0.8209597  416
+[4] {AR=1}      => {MG=1} 0.01973809 0.03867968 0.8209597  416
+[5] {MG=1}      => {ET=1} 0.01551528 0.32930514 0.7716739  327
+[6] {ET=1}      => {MG=1} 0.01551528 0.03635757 0.7716739  327
+[7] {ET=1,MG=1} => {AR=1} 0.01096033 0.70642202 1.3843376  231
+[8] {AR=1,MG=1} => {ET=1} 0.01096033 0.55528846 1.3012297  231
+[9] {AR=1,ET=1} => {MG=1} 0.01096033 0.03944672 0.8372398  231
+[1] "JTExpert"
+Apriori
+
+Parameter specification:
+ confidence minval smax arem  aval originalSupport maxtime support minlen
+       0.01    0.1    1 none FALSE            TRUE       5    0.01      2
+ maxlen target   ext
+     10  rules FALSE
+
+Algorithmic control:
+ filter tree heap memopt load sort verbose
+    0.1 TRUE TRUE  FALSE TRUE    2    TRUE
+
+Absolute minimum support count: 50
+
+set item appearances ...[4 item(s)] done [0.00s].
+set transactions ...[4 item(s), 5040 transaction(s)] done [0.00s].
+sorting and recoding items ... [4 item(s)] done [0.00s].
+creating transaction tree ... done [0.00s].
+checking subsets of size 1 2 3 4 done [0.00s].
+writing ... [28 rule(s)] done [0.00s].
+creating S4 object  ... done [0.00s].
+     lhs                 rhs    support   confidence lift     count
+[1]  {ET=1}           => {AR=1} 0.9182540 0.9895232  1.024065 4628
+[2]  {AR=1}           => {ET=1} 0.9182540 0.9503080  1.024065 4628
+[3]  {SE=1}           => {AR=1} 0.9125000 0.9858521  1.020266 4599
+[4]  {AR=1}           => {SE=1} 0.9125000 0.9443532  1.020266 4599
+[5]  {SE=1}           => {ET=1} 0.8875000 0.9588424  1.033262 4473
+[6]  {ET=1}           => {SE=1} 0.8875000 0.9563823  1.033262 4473
+[7]  {ET=1,SE=1}      => {AR=1} 0.8833333 0.9953052  1.030049 4452
+[8]  {AR=1,SE=1}      => {ET=1} 0.8833333 0.9680365  1.043170 4452
+[9]  {AR=1,ET=1}      => {SE=1} 0.8833333 0.9619706  1.039299 4452
+[10] {MG=1}           => {AR=1} 0.4063492 0.9995120  1.034403 2048
+[11] {AR=1}           => {MG=1} 0.4063492 0.4205339  1.034403 2048
+[12] {MG=1}           => {SE=1} 0.4007937 0.9858468  1.065095 2020
+[13] {SE=1}           => {MG=1} 0.4007937 0.4330118  1.065095 2020
+[14] {MG=1,SE=1}      => {AR=1} 0.4005952 0.9995050  1.034395 2019
+[15] {AR=1,MG=1}      => {SE=1} 0.4005952 0.9858398  1.065087 2019
+[16] {AR=1,SE=1}      => {MG=1} 0.4005952 0.4390085  1.079845 2019
+[17] {MG=1}           => {ET=1} 0.3996032 0.9829185  1.059207 2014
+[18] {ET=1}           => {MG=1} 0.3996032 0.4306179  1.059207 2014
+[19] {ET=1,MG=1}      => {AR=1} 0.3994048 0.9995035  1.034394 2013
+[20] {AR=1,MG=1}      => {ET=1} 0.3994048 0.9829102  1.059198 2013
+[21] {AR=1,ET=1}      => {MG=1} 0.3994048 0.4349611  1.069890 2013
+[22] {MG=1,SE=1}      => {ET=1} 0.3946429 0.9846535  1.061076 1989
+[23] {ET=1,MG=1}      => {SE=1} 0.3946429 0.9875869  1.066975 1989
+[24] {ET=1,SE=1}      => {MG=1} 0.3946429 0.4446680  1.093766 1989
+[25] {ET=1,MG=1,SE=1} => {AR=1} 0.3944444 0.9994972  1.034387 1988
+[26] {AR=1,MG=1,SE=1} => {ET=1} 0.3944444 0.9846459  1.061068 1988
+[27] {AR=1,ET=1,MG=1} => {SE=1} 0.3944444 0.9875807  1.066968 1988
+[28] {AR=1,ET=1,SE=1} => {MG=1} 0.3944444 0.4465409  1.098373 1988
+[1] "Randoop"
+Apriori
+
+Parameter specification:
+ confidence minval smax arem  aval originalSupport maxtime support minlen
+       0.01    0.1    1 none FALSE            TRUE       5    0.01      2
+ maxlen target   ext
+     10  rules FALSE
+
+Algorithmic control:
+ filter tree heap memopt load sort verbose
+    0.1 TRUE TRUE  FALSE TRUE    2    TRUE
+
+Absolute minimum support count: 7650
+
+set item appearances ...[3 item(s)] done [0.00s].
+set transactions ...[3 item(s), 765023 transaction(s)] done [0.23s].
+sorting and recoding items ... [3 item(s)] done [0.01s].
+creating transaction tree ... done [0.09s].
+checking subsets of size 1 2 3 done [0.00s].
+writing ... [9 rule(s)] done [0.00s].
+creating S4 object  ... done [0.10s].
+    lhs            rhs    support    confidence lift      count
+[1] {ET=1}      => {AR=1} 0.71721896 0.9899827  1.0077703 548689
+[2] {AR=1}      => {ET=1} 0.71721896 0.7301057  1.0077703 548689
+[3] {MG=1}      => {AR=1} 0.12140681 0.9547595  0.9719142  92879
+[4] {AR=1}      => {MG=1} 0.12140681 0.1235882  0.9719142  92879
+[5] {MG=1}      => {ET=1} 0.07558204 0.5943873  0.8204373  57822
+[6] {ET=1}      => {MG=1} 0.07558204 0.1043265  0.8204373  57822
+[7] {ET=1,MG=1} => {AR=1} 0.07241089 0.9580437  0.9752574  55396
+[8] {AR=1,MG=1} => {ET=1} 0.07241089 0.5964319  0.8232594  55396
+[9] {AR=1,ET=1} => {MG=1} 0.07241089 0.1009607  0.7939682  55396

data/a-priori/apriori-method.R

@@ -0,0 +1,69 @@
+library(arules)
+
+tools <- c('evosuite', 'jtexpert', 'randoop')
+
+print('EvoSuite')
+file <- read.csv("method/evosuite.csv")
+
+for (i in 1:6) {
+  file[, i] <- as.factor(file[, i])
+}
+
+rules <-
+  apriori(
+    file,
+    parameter = list(
+      support = 0.01,
+      confidence = 0.01,
+      minlen = 2
+    ),
+    appearance = list(
+      both = c("AR=1", "ET=1", "MG=1", "SE=1", "RO=1")
+    )
+  )
+rules.count <- sort(rules, decreasing = TRUE, na.last = NA, by = "count")
+inspect(rules.count)
+
+print('JTExpert')
+file <- read.csv("method/jtexpert.csv")
+
+for (i in 1:6) {
+  file[, i] <- as.factor(file[, i])
+}
+
+rules <-
+  apriori(
+    file,
+    parameter = list(
+      support = 0.01,
+      confidence = 0.01,
+      minlen = 2
+    ),
+    appearance = list(
+      both = c("AR=1", "ET=1", "MG=1", "SE=1")
+    )
+  )
+rules.count <- sort(rules, decreasing = TRUE, na.last = NA, by = "count")
+inspect(rules.count)
+
+print('Randoop')
+file <- read.csv("method/randoop.csv")
+
+for (i in 1:6) {
+  file[, i] <- as.factor(file[, i])
+}
+
+rules <-
+  apriori(
+    file,
+    parameter = list(
+      support = 0.01,
+      confidence = 0.01,
+      minlen = 2
+    ),
+    appearance = list(
+      both = c("AR=1", "ET=1", "MG=1")
+    )
+  )
+rules.count <- sort(rules, decreasing = TRUE, na.last = NA, by = "count")
+inspect(rules.count)