An Empirical Study of Fault Prediction with Code Clone Metrics
Yasutaka Kamei, Hiroki Sato, Akito Monden, Shinji Kawaguchi, Hidetake Uwano, Masataka Nagura, Kenichi Matsumoto, Naoyasu Ubayashi
Abstract
Yasutaka Kamei, Hiroki Sato, Akito Monden, Shinji Kawaguchi, Hidetake Uwano, Masataka Nagura, Kenichi Matsumoto, Naoyasu Ubayashi
Abstract
In this paper, we present a replicated study to predict fault-prone modules with code clone metrics to follow Baba's experiment. We empirically evaluated the performance of fault prediction models with clone metrics using 3 datasets from the Eclipse project and compared it to fault prediction without clone metrics. Contrary to the original Baba's experiment, we could not significantly support the effect of clone metrics, i.e., the result showed that F1-measure of fault prediction was not improved by adding clone metrics to the prediction model. To explain this result, this paper analyzed the relationship between clone metrics and fault density. The result suggested that clone metrics were effective in fault prediction for large modules but not for small modules.
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In this paper, we present a replicated study to predict fault-prone modules with code clone metrics to follow Baba's experiment. We empirically evaluated the performance of fault prediction models with clone metrics using 3 datasets from the Eclipse project and compared it to fault prediction without clone metrics. Contrary to the original Baba's experiment, we could not significantly support the effect of clone metrics, i.e., the result showed that F1-measure of fault prediction was not improved by adding clone metrics to the prediction model. To explain this result, this paper analyzed the relationship between clone metrics and fault density. The result suggested that clone metrics were effective in fault prediction for large modules but not for small modules.
Key concepts: clone (Java method), Eclipse, Computer science, Fault (geology), Software fault tolerance, Data mining, Distributed computing, Fault tolerance