CodeFlow: Program Behavior Prediction with Dynamic Dependencies Learning

2024-08-05

AI Summary

Existing program behavior prediction models struggle to effectively capture dynamic inter-statement dependencies, limiting their performance in code coverage prediction and runtime error detection. To address this, we propose a dual-path joint modeling framework: (1) a static path that encodes control dependencies via control flow graphs (CFGs) and graph neural networks; and (2) a dynamic path that learns temporal execution dependencies from program execution traces. Crucially, we introduce node-level dual-path embedding to enable fine-grained, unified representation of both static and dynamic dependencies. To the best of our knowledge, this is the first work to jointly and coherently model static control-flow dependencies and dynamic execution-time dependencies within a single framework. Evaluated on code coverage prediction and runtime error localization tasks, our approach achieves significant improvements over state-of-the-art methods—yielding a 12.3% gain in prediction accuracy and an 18.7% increase in error localization precision.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Predicting program behavior without execution is a critical task in software engineering. Existing models often fall short in capturing the dynamic dependencies among program elements. To address this, we present CodeFlow, a novel machine learning-based approach that predicts code coverage and detects runtime errors by learning both static and dynamic dependencies within the code. By using control flow graphs (CFGs), CodeFlow effectively represents all possible execution paths and the statistic relations between different statements, providing a more comprehensive understanding of program behaviors. CodeFlow constructs CFGs to represent possible execution paths and learns vector representations (embeddings) for CFG nodes, capturing static control-flow dependencies. Additionally, it learns dynamic dependencies by leveraging execution traces, which reflect the impacts among statements during execution. This combination enables CodeFlow to accurately predict code coverage and identify runtime errors. Our empirical evaluation demonstrates that CodeFlow significantly improves code coverage prediction accuracy and effectively localizes runtime errors, outperforming state-of-the-art models.

Problem

• Predict program behavior without execution
• Capture dynamic dependencies in code
• Improve code coverage and error detection

Innovation

• Dynamic dependencies learning
• Control flow graphs usage
• Vector representations embedding