arXiv:2606.23697v1 Announce Type: cross Abstract: Semantic segmentation of code written in a C-family language remains a challenging problem, due to the language's complex syntax, macro expansion, and irregular structural patterns. Existing chunking methods, such as fixed-sized windows, heuristic splitting, and syntax-based tools, often fail to capture meaningful functional units, limiting the efficacy of retrieval and other downstream LLM driven tasks. In this paper, we address the problem of chunking in C-related languages. First, we define a set of code chunk categories. Second, we train an
The increasing complexity of C-family language codebases and the growing reliance on LLMs for code understanding and generation are driving the need for more effective code segmentation methods.
Improved semantic segmentation of C code can significantly enhance the performance of LLM-driven development tools, impacting fields like cybersecurity, embedded systems, and critical infrastructure.
This research introduces a more granular and semantically aware method for breaking down C code, moving beyond simplistic chunking techniques that limit AI's understanding of complex programming constructs.
- · Software Developers (C/C++)
- · Cybersecurity Sector
- · AI-powered Code Tool Vendors
- · Embedded Systems Industry
- · Traditional Static Analysis Tools
- · LLMs without advanced code chunking capabilities
Downstream LLM tasks like code completion, bug detection, and vulnerability analysis will see marked improvements in C-family languages.
This could accelerate the adoption of AI-assisted development in high-stakes domains reliant on C/C++, potentially reducing development cycles and improving code quality.
Enhanced AI understanding of C code might open new avenues for automated exploit generation and reverse engineering, necessitating rapid advancements in defensive AI capabilities.
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Read at arXiv cs.AI