grounding-protection transformation

This work investigates the feasibility of semantic-preserving code transformations (SPTs) as mutation operators to enhance the test robustness of defect detection models. Inspired by metamorphic testing, we systematically construct and manually validate 16 truly semantics-preserving SPT operators, and evaluate their impact on fine-tuned VulBERTa and PLBART models using the Devign dataset. We reveal—through rigorous validation—that only 16 out of 94 publicly available transformations are genuinely semantics-preserving, exposing significant reuse risks. Experiments show that direct application of SPTs fails to improve model accuracy, and mainstream LLM-based defect detectors exhibit low sensitivity to existing SPTs. Our contributions are threefold: (1) the first systematic empirical study of SPTs for testing-phase defect detection; (2) a reusable, human-validated SPT operator suite; and (3) the finding that current SPTs offer limited utility for improving LLM-based defect detection performance—establishing a critical benchmark and cautionary insight for future robustness evaluation.

This work addresses the challenge that syntactic manipulation of effectful code in multi-stage programming can disrupt evaluation order, leading to semantic discrepancies with non-staged programs. To resolve this, the paper introduces λ²_ref, a statically typed two-level calculus equipped with an automatic let-insertion mechanism. It integrates a lightweight type and effect system to track control effects and establishes a binary logical relation to prove a strong semantic preservation theorem. The study rigorously characterizes when staging annotations preserve semantics, providing—for the first time—a formal guarantee of semantic equivalence for multi-stage programs featuring mutable references. The meta-theory is fully mechanized, thereby laying a solid theoretical foundation for semantic-preserving multi-stage programming.

This work proposes a novel approach to program transformation—such as compiler optimizations—by systematically integrating non-determinism and partially defined operations from functional logic programming. Leveraging the Curry language and its FlatCurry intermediate representation, the method expresses transformation rules in a concise, declarative style through pattern matching and non-deterministic computation. Traditional approaches often involve intricate manipulations of intermediate representations like abstract syntax trees, leading to implementations that are both cumbersome and error-prone. In contrast, the proposed technique enhances expressiveness and readability while maintaining practical feasibility. Experimental evaluation demonstrates that this approach not only preserves code clarity but also achieves competitive performance in real-world transformation tools, offering a compelling alternative for implementing reliable and maintainable program transformations.

This paper addresses the lack of a unified formal framework for modeling operational semantics of programming languages and verifying program correctness. We propose a novel unifying framework based on multi-sorted hybrid modal logic—the first application of such a logic to operational semantics modeling—significantly reducing representational distance in semantic encoding. Compared with dynamic logic, our approach more naturally captures program execution dynamics; relative to traditional weakest precondition calculi, it offers superior expressiveness and semantic clarity. The framework uniformly supports semantic definition, property specification, and formal verification. Crucially, we establish key completeness results, thereby laying a theoretically rigorous foundation that retains practical expressivity for formal program verification.

This work addresses the challenge of efficiently verifying structural optimizations—such as loop unswitching and full loop unrolling—in verified compilers using small-step semantics, which struggles to precisely capture divergence and global control flow. The authors propose a novel hybrid approach that combines small-step and big-step semantics: small-step semantics is employed for local transformations, while coinductive big-step semantics accurately models divergent behaviors and handles structural transformations. An abstract behavioral semantics unifies the interfaces of both styles. This method enables, for the first time, the seamless integration of big-step semantics into CompCert’s predominantly small-step verification framework, achieving end-to-end formal verification of complex loop optimizations without altering the top-level semantic preservation theorem.

This work addresses the prevailing lack of systematic understanding of foundational formal theories in current AI compiler design, which hinders rigorous evaluation of the completeness and desirability of intermediate representations and compilation abstractions. For the first time, it systematically establishes precise correspondences between core mechanisms of MLIR—such as term rewriting systems, refinement calculi, and abstract interpretation—and classical formal theories. By grounding compiler abstractions in formal semantics, the paper clarifies the theoretical underpinnings of these constructs, articulates a precise notion of “design completeness,” and provides assessable criteria and guiding principles to navigate trade-offs between engineering pragmatism and theoretical ideals.

This work addresses the high computational cost of verifying program correctness during continuous software evolution by proposing a syntax-driven incremental verification approach. Built upon operator-precedence grammars and synthesized attribute semantics, the method enables localized re-verification of KernelC programs within a matching logic framework, analyzing only those semantic fragments affected by code changes while avoiding full re-verification. The implemented prototype demonstrates that this strategy significantly reduces verification overhead in most scenarios without compromising the precision or efficiency of formal verification.

本文解决了细粒度代码重构自动化问题，通过形式化五种Move Statement重构方法，并结合现有技术实现更细粒度的表达式移动。

为解决LLM生成代码增加的软件复杂性和维护负担，提出PROOF方法，通过结构化规范间接管理代码库，并证明其在实际仓库中的有效性。

This work proposes a novel reflective protocol that enables runtime upward navigation through the semantic tower—a capability absent in existing runtime systems, which are restricted to downward execution along abstraction layers. By formally integrating operational semantics with runtime reflection, the approach introduces the notion of “first-class implementations” and establishes a generalized safe-point mechanism grounded in formal specifications. This mechanism permits observation and dynamic switching of high-level abstractions’ underlying implementations during execution, thereby overcoming the traditional limitation of supporting only downward compilation or interpretation. The study demonstrates, for the first time, the feasibility and effectiveness of deeply integrating semantic theory with runtime system design, enabling adaptive and semantically aware execution environments.