Rnalytica

An R package of JIRA defect datasets and tool suites for explainable software analytics.

Download and Reference for the JIRA defect datasets

JIRA defect datasets

the JIRA defect datasets can be referenced as:

@inproceedings{yatish2019icse,
    Author={Yatish, Suraj and Jiarpakdee, Jirayus and Thongtanunam, Patanamon and Tantithamthavorn, Chakkrit},
    Title = {Mining Software Defects: Should We Consider Affected Releases?},
    Booktitle = {The International Conference on Software Engineering (ICSE)},
    Year = {2019}
}

Install an Rnalytica R package

To prepare execution enrionment, please run the command below in terminal.

Then, install Rnalytica with the devtools R package:

install.packages('devtools')
devtools::install_github('software-analytics/Rnalytica')

Example R Usage

To load the library:

library(Rnalytica)

To list all 131 defect datasets:

listDataset

To load a defect dataset from the Rnalytica R package:

Data = loadDefectDataset('groovy-1_5_7','jira')

To visualize pair-wise correlations among input metrics and presents using a visualization of the hierarchical cluster analysis:

plotVarClus(dataset = Data$data, metrics = Data$indep)

To automatically remove irrelevant metrics and mitigate correlated metrics with AutoSpearman:

AutoSpearman(dataset = Data$data, metrics = Data$indep)

Example Python Usage (by calling R package)

import rpy2
from rpy2.robjects.packages import importr
from rpy2.robjects import r, pandas2ri, StrVector
pandas2ri.activate()
import pandas as pd
Rnalytica = importr('Rnalytica')

features_names = ["CountDeclMethodPrivate","AvgLineCode","CountLine","MaxCyclomatic","CountDeclMethodDefault",
                  "AvgEssential","CountDeclClassVariable","SumCyclomaticStrict","AvgCyclomatic","AvgLine",
                  "CountDeclClassMethod","AvgLineComment","AvgCyclomaticModified","CountDeclFunction",
                  "CountLineComment","CountDeclClass","CountDeclMethod","SumCyclomaticModified",
                  "CountLineCodeDecl","CountDeclMethodProtected","CountDeclInstanceVariable",
                  "MaxCyclomaticStrict","CountDeclMethodPublic","CountLineCodeExe","SumCyclomatic",
                  "SumEssential","CountStmtDecl","CountLineCode","CountStmtExe","RatioCommentToCode",
                  "CountLineBlank","CountStmt","MaxCyclomaticModified","CountSemicolon","AvgLineBlank",
                  "CountDeclInstanceMethod","AvgCyclomaticStrict","PercentLackOfCohesion","MaxInheritanceTree",
                  "CountClassDerived","CountClassCoupled","CountClassBase","CountInput_Max","CountInput_Mean",
                  "CountInput_Min","CountOutput_Max","CountOutput_Mean","CountOutput_Min","CountPath_Max",
                  "CountPath_Mean","CountPath_Min","MaxNesting_Max","MaxNesting_Mean","MaxNesting_Min",
                  "COMM","ADEV","DDEV","Added_lines","Del_lines","OWN_LINE","OWN_COMMIT","MINOR_COMMIT",
                  "MINOR_LINE","MAJOR_COMMIT","MAJOR_LINE"]
    
data_train = pd.read_csv("datasets/activemq-5.0.0.csv")
X_train = data_train[features_names]

results = Rnalytica.AutoSpearman(dataset = X_train, metrics = rpy2.robjects.StrVector(features_names))
print(results)

['CountDeclMethodPrivate' 'CountDeclMethodDefault' 'AvgEssential'
 'CountDeclClassVariable' 'CountDeclClassMethod' 'AvgLineComment'
 'AvgCyclomaticModified' 'CountDeclClass' 'CountDeclMethodProtected'
 'CountDeclInstanceVariable' 'CountDeclMethodPublic' 'RatioCommentToCode'
 'AvgLineBlank' 'PercentLackOfCohesion' 'MaxInheritanceTree'
 'CountClassDerived' 'CountClassCoupled' 'CountClassBase'
 'CountInput_Mean' 'CountInput_Min' 'CountOutput_Min' 'MaxNesting_Min'
 'ADEV' 'OWN_LINE' 'OWN_COMMIT' 'MINOR_COMMIT' 'MAJOR_LINE']