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Real bug fixes found in open source repositories seem to be the perfectsource for learning to localize and repair real bugs. However, the absence oflarge scale bug fix collections has made it difficult to effectively exploitreal bug fixes in the training of larger neural models in the past. Incontrast, artificial bugs -- produced by mutating existing source code -- canbe easily obtained at a sufficient scale and are therefore often preferred inthe training of existing approaches. Still, localization and repair models thatare trained on artificial bugs usually underperform when faced with real bugs.This raises the question whether bug localization and repair models trained onreal bug fixes are more effective in localizing and repairing real bugs.  We address this question by introducing RealiT, a pre-train-and-fine-tuneapproach for effectively learning to localize and repair real bugs from realbug fixes. RealiT is first pre-trained on a large number of artificial bugsproduced by traditional mutation operators and then fine-tuned on a smaller setof real bug fixes. Fine-tuning does not require any modifications of thelearning algorithm and hence can be easily adopted in various trainingscenarios for bug localization or repair (even when real training data isscarce). In addition, we found that training on real bug fixes with RealiT isempirically powerful by nearly doubling the localization performance of anexisting model on real bugs while maintaining or even improving the repairperformance.

Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes