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Learning-based bug detectors promise to find bugs in large code bases byexploiting natural hints such as names of variables and functions or comments.Still, existing techniques tend to underperform when presented with realisticbugs. We believe bug detector learning to currently suffer from a lack ofrealistic defective training examples. In fact, real world bugs are scarcewhich has driven existing methods to train on artificially created and mostlyunrealistic mutants. In this work, we propose a novel contextual mutationoperator which incorporates knowledge about the mutation context to dynamicallyinject natural and more realistic faults into code. Our approach employs amasked language model to produce a context-dependent distribution over feasibletoken replacements. The evaluation shows that sampling from a language modeldoes not only produce mutants which more accurately represent real bugs butalso lead to better performing bug detectors, both on artificial benchmarks andon real world source code.

DeepMutants: Training neural bug detectors with contextual mutations