title: Learning to Predict Program Execution by Modeling Dynamic Dependency on
    Code Graphs
summary: 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_authors._id: 66b62a3428fa16f1b8a94ae3
paper_authors.name: Cuong Chi Le
paper_authors._id: 66b62a3428fa16f1b8a94ae4
paper_authors.name: Hoang Nhat Phan
paper_authors._id: 66b62a3428fa16f1b8a94ae5
paper_authors.name: Huy Nhat Phan
paper_authors._id: 66b62a3428fa16f1b8a94ae6
paper_authors.name: Tien N. Nguyen
paper_authors._id: 66b62a3428fa16f1b8a94ae7
paper_authors.name: Nghi D. Q. Bui
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-09T17:44:34.620Z
paper_authors._id: 63a44141de134926a2ba0458
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a44141de134926a2ba0458/FP3uxpsKL1nBrrKyiFne3.jpeg
paper_authors.fullname: Nghi Bui
paper_authors.type: user
paper_authors.user: bdqnghi
paper_title: Learning to Predict Program Execution by Modeling Dynamic Dependency on
    Code Graphs
paper_summary: 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_ai_keywords: control flow graphs (CFGs) dynamic dependencies execution traces vector representations static control-flow dependencies