Pattern Trees for Fault-Proneness Detection in Object-Oriented Software

Abstract

Problem statement: This study introduced an application of pattern tree based classification technique in the area of object-oriented software quality estimation. This application explored the fault prediction accuracy of pattern trees. Approach: Similarity measures and fuzzy aggregations employed in the pattern tree technique had been used to generate tree models for fault detection in software modules. Experiments had been performed on datasets namely, KC1 and KC3 obtained from NASA's metric data program. Pattern tree models were built using metrics from the object-oriented software datasets. Results: AND/OR, OWA and WA had been selected for pattern tree induction. Pattern tree models build using RMSE similarity measure produced higher accuracy as compared to other similarity measures. Conclusion: The proposed application succeeded in improving the quality of the object-oriented software in terms of prediction accuracy. Pattern trees models were found to be less structural complex as compared to fuzzy decision trees.