The paper introduces LWVIC4Code, a non‑contrastive, layer‑wise representation learning method for detecting Type‑IV code clones—semantically equivalent fragments that differ syntactically. It builds on the VICReg framework, adding cross‑layer consistency regularization and depth‑dependent weighting to refine semantic information across transformer layers. Experiments on Python and multi‑language datasets show that LWVIC4Code matches or outperforms contrastive baselines and zero‑shot large language models, generalizing well to Java and C# without requiring negative samples.
arXiv:2608. 15412v1 Announce Type: cross Abstract: Encoder-based code representation models remain widely deployed for discriminative tasks such as clone detection and code classification, where their small size and low inference cost are decisive.
The paper investigates whether code large language models (CodeLLMs) inadvertently reproduce proprietary or sensitive code by evaluating seven state‑of‑the‑art training data detection (TDD) methods on eight CodeLLMs. It introduces CodeSnitch, a benchmark of 9,000 function‑level code samples across three languages, each labeled as included or excluded from training data, and applies mutation strategies based on the Type‑1 to Type‑4 code clone taxonomy to test TDD robustness. The study offers a systematic assessment of current TDD techniques for code and suggests directions for developing more effective detection methods.
The paper investigates using synthetic natural-language descriptions to contrastively pretrain small transformer encoders for code representation. By pairing generated descriptions with code in a dual-encoder setup during training and discarding them at inference, the authors achieve significant improvements over traditional pretraining baselines on most evaluated tasks. When fine‑tuned, these models match or surpass much larger zero‑shot models and remain competitive with execution‑aware supervision, indicating a scalable alternative for code embeddings.
The paper introduces LSem2Vec, a two‑stage method that first uses a large language model to extract source code semantics and then applies a sentence embedding model to produce vector representations. This approach removes the need for task‑specific training or fine‑tuning, addressing errors in LLM outputs. Experiments on three datasets across multiple programming languages show that LSem2Vec outperforms five state‑of‑the‑art unsupervised methods.
arXiv:2608. 04215v1 Announce Type: cross Abstract: The growing diversity of code clone types, from syntactic copies to cross-language semantic clones to AI-generated duplicates, has created a fragmentation crisis in clone detection.
arXiv:2307.10631v2 Announce Type: replace-cross Abstract: The practice of code reuse is crucial in software development for a faster and more efficient development lifecycle. In reality, however, cod...
The paper introduces CETBench, a benchmark for assessing large language models (LLMs) on code-equivalence checking by generating program pairs through random code transformations. It demonstrates that even simple transformations significantly degrade LLM performance, especially in cross-lingual scenarios, and proposes a fine-tuning approach to improve results. The study provides detailed ablations on program difficulty and transformation types, offering insights into LLMs’ limited semantic understanding of code.
arXiv:2608. 02084v1 Announce Type: cross Abstract: Binary function embedding models are trained to encode the semantics of binary code in such a way that they can be generalized to a variety of reverse engineering tasks, such as binary code search, vulnerability detection, or malware classification.
The paper introduces SaraCoder, a retrieval‑augmented framework that improves knowledge density for repository‑level code completion by pruning redundant context and selectively injecting cross‑file dependency definitions within a fixed token budget. It employs a Hierarchical Feature Optimization module to reduce an over‑complete candidate pool in four stages, freeing tokens for an External‑Aware Identifier Disambiguator. Experiments on CrossCodeEval and RepoEval‑Updated demonstrate that SaraCoder outperforms strong RAG baselines such as GraphCoder and Repocoder across multiple code LLMs without increasing token usage, and achieves synergistic gains when combined with other cross‑file methods.
arXiv:2509. 23449v2 Announce Type: replace Abstract: Binary code similarity detection is a core task in reverse engineering.
arXiv:2505. 03818v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.
arXiv:2512. 22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs.