IEEE Transactions on Software Engineering - Special issue on software reliability
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Selected papers of the sixth annual Oregon workshop on Software metrics
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
The prediction of faulty classes using object-oriented design metrics
Journal of Systems and Software
Elements of Software Science (Operating and programming systems series)
Elements of Software Science (Operating and programming systems series)
Predicting Fault-Prone Modules with Case-Based Reasoning
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
Fuzzy logic techniques for software reliability engineering
Fuzzy logic techniques for software reliability engineering
Application of multivariate analysis for software fault prediction
Software Quality Control
Application of neural networks for software quality prediction using object-oriented metrics
Journal of Systems and Software
An empirical study of predicting software faults with case-based reasoning
Software Quality Control
A Fault Prediction Model with Limited Fault Data to Improve Test Process
PROFES '08 Proceedings of the 9th international conference on Product-Focused Software Process Improvement
Information Sciences: an International Journal
Review: A systematic review of software fault prediction studies
Expert Systems with Applications: An International Journal
Deviance from perfection is a better criterion than closeness to evil when identifying risky code
Proceedings of the IEEE/ACM international conference on Automated software engineering
Sequence diagram to colored Petri nets transformation testing: an immune system metaphor
Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research
Example-based model-transformation testing
Automated Software Engineering
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
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Predicting fault-prone modules for software development projects enables companies to reach high reliable systems and minimizes necessary budget, personnel and resource to be allocated to achieve this goal. Researchers have investigated various statistical techniques and machine learning algorithms until now but most of them applied their models to the different datasets which are not public or used different criteria to decide the best predictor model. Artificial Immune Recognition System is a supervised learning algorithm which has been proposed in 2001 for the classification problems and its performance for UCI datasets (University of California machine learning repository) is remarkable. In this paper, we propose a novel software defect prediction model by applying Artificial Immune Recognition System (AIRS) along with the Correlation-Based Feature Selection (CFS) technique. In order to evaluate the performance of the proposed model, we apply it to the five NASA public defect datasets and compute G-mean 1, G-mean 2 and F-measure values to discuss the effectiveness of the model. Experimental results show that AIRS has a great potential for software defect prediction and AIRS along with CFS technique provides relatively better prediction for large scale projects which consist of many modules.