LExecution: Learning to Guide and Analyze Program Executions

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Neural software analysis has become an effective way of complementing and improving traditional, logic-based program analysis. Almost all of today’s neural software analyses focus on source code and other static artifacts associated with software. In contrast, little work has been done toward exploiting the power of large-scale machine learning in combination with dynamic program analysis. This proposal will use learning-based techniques to enable dynamic analysis, reason about program executions, and eventually improve the source code of the program. To this end, we plan to explore three research directions: (1) Learning-guided execution, which uses machine learning models to enable dynamic analysis in situations where a regular execution would get stuck. (2) Making predictions about executions, e.g., by identifying misbehavior and likely bugs based on information available at runtime. (3) Execution-guided code editing, which predicts how to improve the source code of a program based not only on the code, but also on traces of its execution. Overall, this project will close the gap between dynamic program analysis and neural software analysis, and if successful, yield novel analysis techniques that outperform the state-of-the-art.