The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
Software Defect Association Mining and Defect Correction Effort Prediction
IEEE Transactions on Software Engineering
Software defect prediction using relational association rule mining
Information Sciences: an International Journal
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This paper proposes a fault-prone module prediction method that combines association rule mining with logistic regression analysis. In the proposed method, we focus on three key measures of interestingness of an association rule (support, confidence and lift) to select useful rules for the prediction. If a module satisfies the premise (i.e. the condition in the antecedent part) of one of the selected rules, the module is classified by the rule as either fault-prone or not. Otherwise, the module is classified by the logistic model. We experimentally evaluated the prediction performance of the proposed method with different thresholds of each rule interestingness measure (support, confidence and lift) using a module set in the Eclipse project, and compared it with three well-known fault-proneness models (logistic regression model, linear discriminant model and classification tree). The result showed that the improvement of the F1-value of the proposed method was 0.163 at maximum compared to conventional models.