Towards a metrics suite for object oriented design
OOPSLA '91 Conference proceedings on Object-oriented programming systems, languages, and applications
Practical software metrics for project management and process improvement
Practical software metrics for project management and process improvement
Ordinal association rules for error identification in data sets
Proceedings of the tenth international conference on Information and knowledge management
Machine Learning
Assessing the applicability of fault-proneness models across object-oriented software projects
IEEE Transactions on Software Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CSMR '01 Proceedings of the Fifth European Conference on Software Maintenance and Reengineering
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Robust Prediction of Fault-Proneness by Random Forests
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Empirical Assessment of Machine Learning based Software Defect Prediction Techniques
WORDS '05 Proceedings of the 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Software Structure Metrics Based on Information Flow
IEEE Transactions on Software Engineering
A hybrid faulty module prediction using association rule mining and logistic regression analysis
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Techniques for evaluating fault prediction models
Empirical Software Engineering
Aspect mining using self-organizing maps with method level dynamic software metrics as input vectors
Aspect mining using self-organizing maps with method level dynamic software metrics as input vectors
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A General Software Defect-Proneness Prediction Framework
IEEE Transactions on Software Engineering
Software defect detection with rocus
Journal of Computer Science and Technology
Sample-based software defect prediction with active and semi-supervised learning
Automated Software Engineering
Special issue on repeatable results in software engineering prediction
Empirical Software Engineering
Searching for rules to detect defective modules: A subgroup discovery approach
Information Sciences: an International Journal
Generalized association rule mining with constraints
Information Sciences: an International Journal
Evaluating defect prediction approaches: a benchmark and an extensive comparison
Empirical Software Engineering
A Systematic Literature Review on Fault Prediction Performance in Software Engineering
IEEE Transactions on Software Engineering
Mining numerical association rules via multi-objective genetic algorithms
Information Sciences: an International Journal
FARP: Mining fuzzy association rules from a probabilistic quantitative database
Information Sciences: an International Journal
A fuzzy classifier approach to estimating software quality
Information Sciences: an International Journal
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This paper focuses on the problem of defect prediction, a problem of major importance during software maintenance and evolution. It is essential for software developers to identify defective software modules in order to continuously improve the quality of a software system. As the conditions for a software module to have defects are hard to identify, machine learning based classification models are still developed to approach the problem of defect prediction. We propose a novel classification model based on relational association rules mining. Relational association rules are an extension of ordinal association rules, which are a particular type of association rules that describe numerical orderings between attributes that commonly occur over a dataset. Our classifier is based on the discovery of relational association rules for predicting whether a software module is or it is not defective. An experimental evaluation of the proposed model on the open source NASA datasets, as well as a comparison to similar existing approaches is provided. The obtained results show that our classifier overperforms, for most of the considered evaluation measures, the existing machine learning based techniques for defect prediction. This confirms the potential of our proposal.