Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Software Engineering Research
Abstract
This paper introduces a novel approach to large-scale code analysis leveraging hierarchical abstraction learning. The core idea is to accelerate code analysis by learning hierarchical representations of code structures, effectively capturing semantic relationships across multiple levels of granularity. A reinforcement learning agent is employed to iteratively refine a hierarchical tree representation of the code, guided by metrics encompassing code complexity, data flow, and control flow. Unlike traditional static analysis methods, this approach dynamically adapts to the code's structure through learned abstractions, leading to improved efficiency and accuracy in code understanding. The proposed method offers a significant advancement in code analysis techniques, particularly for large and complex software systems.
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
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A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
