Dynamic Topological Dependency Graph Learning (DTTL)

Abstract

This paper introduces Dynamic Topological Dependency Graph Learning (DTTL), a novel approach to program dependency analysis that leverages reinforcement learning to automatically construct and refine dependency graphs without manual intervention. Traditional static dependency analysis tools are limited in their ability to adapt to the dynamic nature of software execution. DTTL addresses this limitation by integrating dependency learning directly into the program's runtime, using feedback from code execution to dynamically adjust and update the topological representation of dependencies. The core mechanism utilizes a reinforcement learning algorithm to learn optimal dependency relationships based on execution outcomes – success or failure, and performance metrics. The resulting graph accurately reflects the state of the program's execution and continuously evolves alongside the program's development. This approach offers significant improvements in accuracy and adaptability compared to static analysis, paving the way for more intelligent and responsive software development tools.