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Edit and run



In [2]:

    
%load_ext rmagic









    



During startup - Warning messages:
1: Setting LC_TIME failed, using "C" 
2: Setting LC_MONETARY failed, using "C" 
3: Setting LC_PAPER failed, using "C" 
4: Setting LC_MEASUREMENT failed, using "C"



In [3]:

    
%%R

dir = "24-04-2014"
mycvs = paste(dir, "robust_program_events.csv", sep="/")
robust_program_events = read.csv(mycvs, sep="\t")


mycvs = paste(dir, "buggy_program_events.csv", sep="/")
buggy_program_events = read.csv(mycvs, sep="\t")

print(nrow(robust_program_events))
print(nrow(buggy_program_events))

TODO: Add an explanation about program traces as documents.

Now, we load the tm package and create the corpuses from the "documents"



In [4]:

    
%%R

library(tm)

robust_corpus = Corpus(VectorSource(robust_program_events[,1]))
buggy_corpus  = Corpus(VectorSource(buggy_program_events[,1]))

print(robust_corpus)
print(buggy_corpus)









    





A corpus with 100 text documents
A corpus with 187 text documents

Now, it is time to create the document matrixes, and convert them to data frames adding its classes.



In [7]:

    
%%R

robust_dm = DocumentTermMatrix(robust_corpus)
buggy_dm  = DocumentTermMatrix(buggy_corpus)

#print(robust_dm)
#print(buggy_dm)

sink("/dev/null")

robust_dm_df =  as.data.frame(inspect(robust_dm))
rownames(robust_dm_df) = 1:nrow(robust_dm)
robust_dm_df["class"] = "robust"
    
buggy_dm_df =  as.data.frame(inspect(buggy_dm))
rownames(buggy_dm_df) = 1:nrow(buggy_dm)
buggy_dm_df["class"] = "buggy"
    
sink()

#print(colnames(robust_dm_df))
#print(colnames(buggy_dm_df))

but we need make sure we are using the same variables for both corpuses.



In [8]:

    
%%R

dm_df = merge(robust_dm_df, buggy_dm_df,all=TRUE, sort=FALSE) 

#print(dm_df[1,])
#print(nrow(dm_df))
dm_df[is.na(dm_df)] = 0

robust_cases = dm_df[dm_df$class == "robust",]
buggy_cases  = dm_df[dm_df$class == "buggy",]

print(nrow(robust_cases))
print(nrow(buggy_cases))

now, we are ready to select train and test..



In [12]:

    
%%R

# buggy train and test

n = nrow(buggy_cases)
#rsample = sample(n)

# 100 cases are selected to keep the train dataset balanced
train_sample = 1:75 #rsample[1:as.integer(n*0.45)] 
test_sample = 76:n #rsample[as.integer(n*0.45+1):n]

buggy_train = buggy_cases[train_sample,]
buggy_test  = buggy_cases[test_sample,]

print(nrow(buggy_train))
print(nrow(buggy_test))

# robust train and test

n = nrow(robust_cases)
#rsample = sample(n)

# 100 cases are selected to keep the train dataset balanced
train_sample = 1:75#rsample[1:as.integer(n*0.75)]
test_sample = 76:n #rsample[as.integer(n*0.75+1):n]

robust_train = robust_cases[train_sample,]
robust_test  = robust_cases[test_sample,]

print(nrow(robust_train))
print(nrow(robust_test))

train = rbind(buggy_train, robust_train)
test  = rbind(buggy_test, robust_test)









    





[1] 75
[1] 112
[1] 75
[1] 25

Finally, we are ready to train and test a knn model:



In [16]:

    
%%R

#print(round(importance(rf),2))

library("class")

x_train = train[,names(train) != "class"]
x_test  = test[,names(test) != "class"]
y_train = train[,"class"]
y_test  = test[,"class"]

#print(y)
for (k in 1:10) {
  print(k)
  z = knn(x_train,x_test, y_train, k, use.all = FALSE)
  #print(z)
  #print(test[,"class"])
  print(table(z, y_test))
}









    





[1] 1
        y_test
z        buggy robust
  buggy     89      0
  robust    23     25
[1] 2
        y_test
z        buggy robust
  buggy     89      0
  robust    23     25
[1] 3
        y_test
z        buggy robust
  buggy     86      0
  robust    26     25
[1] 4
        y_test
z        buggy robust
  buggy     92      0
  robust    20     25
[1] 5
        y_test
z        buggy robust
  buggy     96      0
  robust    16     25
[1] 6
        y_test
z        buggy robust
  buggy    100      0
  robust    12     25
[1] 7
        y_test
z        buggy robust
  buggy    100      0
  robust    12     25
[1] 8
        y_test
z        buggy robust
  buggy    100      9
  robust    12     16
[1] 9
        y_test
z        buggy robust
  buggy    100     10
  robust    12     15
[1] 10
        y_test
z        buggy robust
  buggy    100     15
  robust    12     10

or a random forest..



In [33]:

    
%%R
library("e1071")

xy_train = train#[,names(train) != "class"]
xy_train[,"class"] = factor(train[,"class"])
x_test = test[,names(test) != "class"]
#y_train = train[,"class"]
y_test  = test[,"class"]

m = svm(class ~., data=xy_train, gamma=0.001, cost=10)
#tuned = tune.svm(class~., data = xy_train,  gamma = 10^(-6:-1), cost = 10^(1:2))
#print(summary(tuned))
z = predict(m,x_test)
print(table(z, y_test))









    





        y_test
z        buggy robust
  buggy     76      0
  robust    36     25