https://github.com/FSoft-AI4Code/CodeFlow

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This is an automated message from the Librarian Bot. I found the following papers similar to this paper.

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Papers

arxiv:2408.02816

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

Published on Aug 5, 2024

· Submitted by

Nghi Bui on Aug 9, 2024

Upvote

Authors:

Nghi D. Q. Bui

Abstract

A machine learning framework named CodeFlowrepresents uses dynamic dependencies learning to predict code coverage and detect runtime errors by analyzing control flow graphs and execution traces.

AI-generated 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.