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

Understand

The execution behavior of a program often depends on external resources, such as program inputs or file contents, and so cannot be run in isolation.

• Nevertheless, software developers benefit from fast iteration loops where automated tools identify errors as early as possible, even before programs can be compiled and run.
• This presents an interesting machine learning challenge: can we predict runtime errors in a "static" setting, where program execution is not possible? Here, we introduce a real-world dataset and task for predicting runtime errors, which we show is difficult for generic models like Transformers.
• We approach this task by developing an interpreter-inspired architecture with an inductive bias towards mimicking program executions, which models exception handling and "learns to execute" descriptions of the contents of external resources.