Within the scope of the case studies, different ML-based models were constructed and applied by using WEKA tool with the version of 3.8.5. Case studies #1 and #2 were performed in the context of the most known public repository for SDP, NASA Metrics Data Program (MDP). The classifiers used for the experiments are summarized below: Artificial Neural Network (ANN): weka.classifiers.functions.MultilayerPerceptron Bayesian Belief Network (BBN): weka.classifiers.bayes.BayesNet Decision Tree (DT): weka.classifiers.trees.REPTree Fuzzy Rule Based (FRBC): weka.classifiers.rules.MultiObjectiveEvolutionaryFuzzyClassifier Logistic Regressin (LogR): weka.classifiers.functions.SimpleLogistic Naïve Bayes (NB): weka.classifiers.bayes.NaiveBayes S...
During the last 10 years, hundreds of different defect prediction models have been published. The pe...
Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in s...
Software defect prediction strives to improve software quality and testing efficiency by constructin...
Within the scope of the case studies, different ML-based models were constructed and applied by usin...
Within the scope of the case study, different ML-based models were constructed and applied on NASA d...
Failure of software systems as a result of software testing is very much rampant as modern software ...
Software defect prediction is crucial used for detecting possible defects in software before they ma...
The software systems of modern computers are extremely complex and versatile. Therefore, it is essen...
Abstract: During software development and maintenance, predicting software bugs becomes critical. De...
Software defect prediction aims at detecting part of software that can likely contain faulty modules...
Abstract—Defect prediction models help software quality as-surance teams to effectively allocate the...
Predicting when and where bugs will appear in software may assist improve quality and save on softwa...
Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC)...
The feasibility of building a software defect prediction (SDP) model in the absence of previous reco...
This dataset is about a systematic review of unsupervised learning techniques for software defect pr...
During the last 10 years, hundreds of different defect prediction models have been published. The pe...
Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in s...
Software defect prediction strives to improve software quality and testing efficiency by constructin...
Within the scope of the case studies, different ML-based models were constructed and applied by usin...
Within the scope of the case study, different ML-based models were constructed and applied on NASA d...
Failure of software systems as a result of software testing is very much rampant as modern software ...
Software defect prediction is crucial used for detecting possible defects in software before they ma...
The software systems of modern computers are extremely complex and versatile. Therefore, it is essen...
Abstract: During software development and maintenance, predicting software bugs becomes critical. De...
Software defect prediction aims at detecting part of software that can likely contain faulty modules...
Abstract—Defect prediction models help software quality as-surance teams to effectively allocate the...
Predicting when and where bugs will appear in software may assist improve quality and save on softwa...
Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC)...
The feasibility of building a software defect prediction (SDP) model in the absence of previous reco...
This dataset is about a systematic review of unsupervised learning techniques for software defect pr...
During the last 10 years, hundreds of different defect prediction models have been published. The pe...
Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in s...
Software defect prediction strives to improve software quality and testing efficiency by constructin...