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Various software fault prediction models and techniques for buildingalgorithms have been proposed. Many studies have compared and evaluated them toidentify the most effective ones. However, in most cases, such models andtechniques do not have the best performance on every dataset. This is becausethere is diversity of software development datasets, and therefore, there is arisk that the selected model or technique shows bad performance on a certaindataset. To avoid selecting a low accuracy model, we apply bandit algorithms topredict faults. Consider a case where player has 100 coins to bet on severalslot machines. Ordinary usage of software fault prediction is analogous to theplayer betting all 100 coins in one slot machine. In contrast, banditalgorithms bet one coin on each machine (i.e., use prediction models)step-by-step to seek the best machine. In the experiment, we developed anartificial dataset that includes 100 modules, 15 of which include faults. Then,we developed various artificial fault prediction models and selected themdynamically using bandit algorithms. The Thomson sampling algorithm showed thebest or second-best prediction performance compared with using only oneprediction model.

A Simulation Study of Bandit Algorithms to Address External Validity of Software Fault Prediction