Learning to Predict Program Execution by Modeling Dynamic Dependency on Code Graphs

Cuong Chi Le, Hoang Nhat Phan, Huy Nhat Phan, Tien N. Nguyen, Nghi Bui

Learning to Predict Program Execution by Modeling Dynamic Dependency on Code Graphs: 5 upvotes on Hugging Face Daily Papers, #12 of 13 papers on 2024-08-09. Day-by-day upvote history.

Predicting program behavior without execution is an essential and challenging task in software engineering. Traditional models often struggle to capture dynamic dependencies and interactions within code. This paper introduces a novel machine learning-based framework called CodeFlowrepresents, which predicts code coverage and detects runtime errors through Dynamic Dependencies Learning. Utilizing control flow graphs (CFGs), CodeFlowrepresents all possible execution paths and the relationships between different statements, offering a comprehensive understanding of program behavior. It constructs CFGs to depict execution paths and learns vector representations for CFG nodes, capturing static control-flow dependencies. Additionally, it learns dynamic dependencies through execution traces, which reflect the impacts among statements during execution. This approach enables accurate prediction of code coverage and identification of runtime errors. Empirical evaluations show significant improvements in code coverage prediction accuracy and effective localization of runtime errors, surpassing current models.

Paper page on Hugging Face · arXiv

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