Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Comparing case-based reasoning classifiers for predicting high risk software components
Journal of Systems and Software
Elements of Software Science (Operating and programming systems series)
Elements of Software Science (Operating and programming systems series)
Software Engineering Measurement
Software Engineering Measurement
An Application of Fuzzy Clustering to Software Quality Prediction
ASSET '00 Proceedings of the 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology (ASSET'00)
Software Quality Classification Modeling Using The SPRINT Decision Tree Algorithm
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Application of Neural Networks for Software Quality Prediction Using Object-Oriented Metrics
ICSM '03 Proceedings of the International Conference on Software Maintenance
Semi-Supervised Learning for Software Quality Estimation
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
An investigation of the effect of module size on defect prediction using static measures
PROMISE '05 Proceedings of the 2005 workshop on Predictor models in software engineering
An empirical study of predicting software faults with case-based reasoning
Software Quality Control
Information Sciences: an International Journal
Object-oriented software fault prediction using neural networks
Information and Software Technology
Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis
Information Sciences: an International Journal
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Software quality estimation with limited fault data: a semi-supervised learning perspective
Software Quality Control
IEEE Transactions on Software Engineering
Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods
IEEE Transactions on Software Engineering
A weighted rough set based method developed for class imbalance learning
Information Sciences: an International Journal
Applying machine learning to software fault-proneness prediction
Journal of Systems and Software
Software defect prediction using artificial immune recognition system
SE'07 Proceedings of the 25th conference on IASTED International Multi-Conference: Software Engineering
Designing of classifiers based on immune principles and fuzzy rules
Information Sciences: an International Journal
Predicting defect-prone software modules using support vector machines
Journal of Systems and Software
A hybrid artificial immune system and Self Organising Map for network intrusion detection
Information Sciences: an International Journal
A Conceptual Framework to Integrate Fault Prediction Sub-Process for Software Product Lines
TASE '08 Proceedings of the 2008 2nd IFIP/IEEE International Symposium on Theoretical Aspects of Software Engineering
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
An antibody network inspired evolutionary framework for distributed object computing
Information Sciences: an International Journal
Estimating software readiness using predictive models
Information Sciences: an International Journal
Immune K-means and negative selection algorithms for data analysis
Information Sciences: an International Journal
Unsupervised learning for expert-based software quality estimation
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Identification of defect-prone classes in telecommunication software systems using design metrics
Information Sciences: an International Journal
Software fault prediction with object-oriented metrics based artificial immune recognition system
PROFES'07 Proceedings of the 8th international conference on Product-Focused Software Process Improvement
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Localizing program logical errors using extraction of knowledge from invariants
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
Transfer learning for cross-company software defect prediction
Information and Software Technology
User preferences based software defect detection algorithms selection using MCDM
Information Sciences: an International Journal
Searching for rules to detect defective modules: A subgroup discovery approach
Information Sciences: an International Journal
Information Sciences: an International Journal
The design of polynomial function-based neural network predictors for detection of software defects
Information Sciences: an International Journal
Creating Process-Agents incrementally by mining process asset library
Information Sciences: an International Journal
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Software quality engineering comprises of several quality assurance activities such as testing, formal verification, inspection, fault tolerance, and software fault prediction. Until now, many researchers developed and validated several fault prediction models by using machine learning and statistical techniques. There have been used different kinds of software metrics and diverse feature reduction techniques in order to improve the models' performance. However, these studies did not investigate the effect of dataset size, metrics set, and feature selection techniques for software fault prediction. This study is focused on the high-performance fault predictors based on machine learning such as Random Forests and the algorithms based on a new computational intelligence approach called Artificial Immune Systems. We used public NASA datasets from the PROMISE repository to make our predictive models repeatable, refutable, and verifiable. The research questions were based on the effects of dataset size, metrics set, and feature selection techniques. In order to answer these questions, there were defined seven test groups. Additionally, nine classifiers were examined for each of the five public NASA datasets. According to this study, Random Forests provides the best prediction performance for large datasets and Naive Bayes is the best prediction algorithm for small datasets in terms of the Area Under Receiver Operating Characteristics Curve (AUC) evaluation parameter. The parallel implementation of Artificial Immune Recognition Systems (AIRS2Parallel) algorithm is the best Artificial Immune Systems paradigm-based algorithm when the method-level metrics are used.