C-ing Clearly: Enhanced C Code Analysis

• C-ing Clearly is a research framework employing semantic anchoring and formal methods for precise analysis and safe transformation of legacy C code.
• It integrates synthetic data generation for LLM training, interactive proofs, and semi-automated porting tools like Checked C and 3C to improve code clarity and safety.
• The approach bridges the semantic gap between source code and binary analysis, enabling enhanced vulnerability detection and formal safety guarantees in C programming.

C-ing Clearly

C-ing Clearly encompasses a set of recent methodologies, theoretical frameworks, and tools that enable precise, semantically grounded analysis or transformation of low-level or legacy C code. These efforts span enhanced binary code explanations using C as an anchor, interactive proofs and verification environments integrated with systems-level C, and formal tool-assisted retrofitting for memory safety. This theme unites research such as C-ing Clearly synthetic data for LLMs, the C* proof language, and 3C's semi-automated Checked C porting, all targeting improved clarity and rigor in the interpretation, transformation, or verification of C code and its compiled forms (Poncu et al., 16 Dec 2025, Cao et al., 3 Apr 2025, Machiry et al., 2022, Li et al., 2022).

1. Motivations for Enhanced Clarity in C Code Analysis

LLMs and human analysts alike face significant challenges with the C language and its derivatives, especially at the binary or assembly level. C code lacks higher-level abstractions for memory management, control structures, and data representations; compiled assembly is even less tractable due to the loss of semantic cues, compiler idiosyncrasies, and code optimization effects (Poncu et al., 16 Dec 2025). Traditional approaches to binary analysis, vulnerability detection, and systems software verification are hampered by the "semantic gap" between source code intent and machine-level realization.

Emerging directions address these issues by synthesizing semantic anchors for machine code (as in C-ing Clearly LLM prompting), integrating proof logic directly into C programming environments (as with C*), or systematically upgrading legacy C to safer dialects (as with Checked C and 3C) (Cao et al., 3 Apr 2025, Machiry et al., 2022). Foundationally, the need for well-defined operational semantics and robust error attribution underpins all these solutions (Li et al., 2022).

2. Synthetic Data Generation: C-ing Clearly for LLM Understanding

The C-ing Clearly method is a data generation pipeline designed to enhance LLM understanding of assembly code via C as a "semantic anchor" (Poncu et al., 16 Dec 2025). The approach proceeds as follows:

1. C/C++ functions are harvested from vulnerability datasets (DiverseVul, VDISC).
2. Each function is compiled to unoptimized x86-64 assembly (gcc -S).
3. A large generator LLM (Llama-3.1-Nemotron-70B-Instruct) is prompted in various configurations:
   • CC (single-turn C-ing Clearly): [C code + ASM code]
   • NOC (No C-ing): [ASM code only]
   • MCC (multi-turn C-ing): C analysis → cross-reference with ASM → final ASM-based analysis (C-free output)
4. Generated analysis reports (for binary code summarization [BCS] and vulnerability detection [VD]) are filtered, especially for ground-truth CWE recall, using rejection sampling.
5. Synthetic {assembly code, report} pairs are used to supervise-fine-tune smaller instruction-tuned models.

Formally, supervised fine-tuning minimizes the standard autoregressive cross-entropy loss

$$\mathcal{L}(\theta) = -\sum_{i}\sum_{t=1}^{T_i}\log p_\theta(y_{i, t}\mid y_{i,<t}, x_i),$$

where $x_i$ encodes assembly (and C context), and $y_i$ is the generated summary or analysis. No auxiliary or contrastive losses are used.

This method yields consistent gains in code summarization and vulnerability detection across multiple model architectures and sizes, demonstrating that semantic anchoring via C enhances LLM capabilities in low-level domains (Poncu et al., 16 Dec 2025).

3. Semi-Automated Porting: 3C and the Checked C Framework

Checked C extends ISO C with checked pointer types (ptr<T>, array_ptr<T>, nt_array_ptr<T>) and associated lightweight bounds annotations, supporting backwards-binary compatibility and incremental safety adoption. Dereference checks are statically verified or dynamically inserted as needed, with compiler optimizations to elide redundant checks (Machiry et al., 2022).

3C is a Clang-based annotation toolchain that automates much of the effort to port legacy C codebases to Checked C. Its pipeline comprises:

• typ3c: Assigns each pointer as checked (chk) or wild via whole-program qualifier inference, localizing unsafety to root causes such as unsafe casts, macro-hiding, or library interop.
• boun3c: Infers array bounds through seeded and propagated flow graphs.
• Root-cause reporting: Prioritizes pointers whose unsafety is the source of downstream "wild" pointers.

The process is iterative: run typ3c/boun3c, refactor or annotate, rerun analyses, and progressively increase the checked coverage. Empirical results show typ3c converts ∼67.9% of pointers, boun3c infers 77.3% of array bounds, and entire conversions (∼40 KLoC) can be completed with a handful of developer runs. The approach discovered both known and new spatial safety bugs in popular systems code (Machiry et al., 2022).

4. Program Verification in C*: C-Centric Proofs and Symbolic Execution

C* embeds full separation-logic specifications, assertions, loop invariants, and proof code blocks directly into C source code, leveraging a forward symbolic-execution engine and an LCF-style higher-order logic proof kernel (Cao et al., 3 Apr 2025). Key features include:

• Inline specification: Use of [[require]], [[ensure]], [[assert]], [[invariant]] attributes, supporting both pre/post-conditions and internal proof goals.
• Symbolic heaps: Program points are annotated with symbolic heaps $$\exists x_1…x_k. (P_1 \land … \land P_m) \land (Q_1 * … * Q_n)$$ tracking both pure and spatial facts.
• Proof API in C: The proof kernel exposes terms (logical formulas) and theorems; users write small C functions as proof tactics, e.g., for commutativity, heap framing, and normalization.
• Residual proof obligations: Any unresolved conditions after symbolic execution become explicit proof goals to be satisfied with additional proof code.

Evaluation includes both micro-benchmarks and challenging real-world systems code (e.g., pKVM buddy allocator). C* demonstrates that complex memory properties, pointer arithmetic, and loop invariants can be verified interactively by programmers in a familiar C-centric environment (Cao et al., 3 Apr 2025).

5. Formal Modeling and Operational Guarantees for Clarity

The formal semantics of Checked C are provided in Coq and PLT Redex models (Li et al., 2022):

• Operational semantics: CoreChkC models pointers as “fat” values of the form $n : n_l : n_h : \tau : m$, parameterized over address, bounds, type, and checkedness. Dereferencing, assignment, and array operations are guarded by these bounds in “checked regions.”
• Blame theorem: Any run-time error in checked code is provably attributable to entry from unchecked code, supporting safe incremental porting.
• Annotation erasure: Fat-pointer annotations can be erased at compile time, with inserted checks sufficing for the same semantics. Compilation to untyped C is shown to preserve safety properties via a simulation theorem.
• Randomized co-validation: Executable models in Redex generate random programs, ensuring consistency between the formal semantics and Clang’s implementation. This process surfaced and enabled the correction of semantic inconsistencies.

The model omits interop-types and generics but demonstrates that clear, explicit formalization of memory safety in C is feasible and that clarity at the semantic level underpins safety guarantees (Li et al., 2022).

6. Limitations and Future Directions

Several intrinsic and practical limitations arise in current approaches:

• The C-ing Clearly method presumes source-level C access for semantic anchoring; pure binaries or heavily optimized code may not be tractable (Poncu et al., 16 Dec 2025).
• Existing data generation pipelines are limited to unoptimized x86-64 and a maximum prompt length; extending to optimized, larger, or other ISA targets is an open area.
• 3C cannot automatically upgrade pointers declared within macros or complex generic structures, and current inference heuristics are limited for non-trivial arithmetic or deeply embedded data flows (Machiry et al., 2022).
• The C* pipeline, while expressive, requires users to write explicit proof code for complex aliasing or structural reasoning, although proof automation libraries provide significant leverage (Cao et al., 3 Apr 2025).
• The operational semantics of Checked C do not yet encompass full language generics or all forms of interop; ongoing efforts are directed at extending the formal model (Li et al., 2022).

A plausible implication is that the unification of semantic anchoring, proof integration, and formal operational modeling is converging toward a more robust, modular, and user-accessible workflow for legacy C code analysis and transformation, but work remains to address scale, generality, and automation.

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References:

• "C-ing Clearly: Enhanced Binary Code Explanations using C code" (Poncu et al., 16 Dec 2025)
• "C*: Unifying Programming and Verification in C" (Cao et al., 3 Apr 2025)
• "C to Checked C by 3C" (Machiry et al., 2022)
• "A Formal Model of Checked C" (Li et al., 2022)