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The execution behavior of a program often depends on external resources, suchas program inputs or file contents, and so cannot be run in isolation.Nevertheless, software developers benefit from fast iteration loops whereautomated tools identify errors as early as possible, even before programs canbe compiled and run. This presents an interesting machine learning challenge:can we predict runtime errors in a "static" setting, where program execution isnot possible? Here, we introduce a real-world dataset and task for predictingruntime errors, which we show is difficult for generic models likeTransformers. We approach this task by developing an interpreter-inspiredarchitecture with an inductive bias towards mimicking program executions, whichmodels exception handling and "learns to execute" descriptions of the contentsof external resources. Surprisingly, we show that the model can also predictthe location of the error, despite being trained only on labels indicating thepresence/absence and kind of error. In total, we present a practical anddifficult-yet-approachable challenge problem related to learning programexecution and we demonstrate promising new capabilities of interpreter-inspiredmachine learning models for code.

Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions