Learning to Predict Program Execution by Modeling Dynamic Dependency on
  Code Graphs
Authors: name: Cuong Chi Le, name: Hoang Nhat Phan, name: Huy Nhat Phan, name: Tien N. Nguyen, name: Nghi D. Q. Bui

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.