With the rapid growth of open-source software, code cloning has become increasingly prevalent. If there are security vulnerabilities in a cloned code segment, those vulnerabilities may spread in the related software to potentially lead to security incidents. The existing methods of vulnerable code detection are performed on the condition that the source code is converted into an intermediate representation. However, these methods do not fully consider the rich semantic knowledge and patch information available for vulnerable codes, which can induce a high false positive rate (FPR). To address this problem, this paper proposes a vulnerable code clone detection method based on code fingerprints, named the Context-enhanced and Patch-validation-based Vulnerable code clone Detector (CPVDetector). A fingerprint database is built for functions, code snippets, and patches derived from preprocessed vulnerable source code. The target code to be detected is firstly transformed into function-level fingerprints. If clone detection fails at this coarse granularity, the detector is then applied at the finer line-level granularity. When fingerprint matching is successful between the target code and the vulnerable code segments, the detector will proceed to verify the context of vulnerable codes. Finally, CPVDetector can verify the fingerprints of patches corresponding to vulnerable codes to further reduce the FPR. Based on the generally accepted classification of code clones, CPVDetector can identify Type 1 and Type 2 vulnerable code clones at the coarse-grained level and offers significantly improved detection sensitivity for Type 3 and Type 4 code clones at the fine-grained level. Experimental results show that the proposed method can achieve high accuracy with a fast detection speed, and the FPR is as low as 2.35%, which is less than one-third of that of other existing methods. In view of its competitive performance and efficiency, CPVDetector can be applied in large-scale vulnerable code detection scenarios.

/pdf/a-novel-vulnerable-code-clone-detector-based-on-context-37v2ldki.pdf

A Novel Vulnerable Code Clone Detector Based on Context Enhancement and Patch Validation

Context: Code Clone Detection (CCD) is a software engineering task that is used for plagiarism detection, code search, and code comprehension. Recently, deep learning-based models have achieved an F1 score (a metric used to assess classifiers) of $\sim$95\% on the CodeXGLUE benchmark. These models require many training data, mainly fine-tuned on Java or C++ datasets. However, no previous study evaluates the generalizability of these models where a limited amount of annotated data is available. Objective: The main objective of this research is to assess the ability of the CCD models as well as few shot learning algorithms for unseen programming problems and new languages (i.e., the model is not trained on these problems/languages). Method: We assess the generalizability of the state of the art models for CCD in few shot settings (i.e., only a few samples are available for fine-tuning) by setting three scenarios: i) unseen problems, ii) unseen languages, iii) combination of new languages and new problems. We choose three datasets of BigCloneBench, POJ-104, and CodeNet and Java, C++, and Ruby languages. Then, we employ Model Agnostic Meta-learning (MAML), where the model learns a meta-learner capable of extracting transferable knowledge from the train set; so that the model can be fine-tuned using a few samples. Finally, we combine contrastive learning with MAML to further study whether it can improve the results of MAML.

pdf/evaluating-few-shot-and-contrastive-learning-methods-for-3qh2k63o.pdf

Evaluating few shot and Contrastive learning Methods for Code Clone Detection

Evaluating few-shot and contrastive learning methods for code clone detection

Can CodeT5 embeddings be adapted for efficient Code Clone Detection and Retrieval?

Identifying similarities in source code is the main challenge for reuse, plagiarism, and code clone detection. Code clone search has emerged as a new research branch in clone detection, aiming to provide similarity search functionality for code snippets. While clone search shares its fundamentals with clone detection, both its objective and requirements differ significantly. Clone search focuses on search engines that are designed to find clones of a single input code snippet (i.e., query) from a large set of code snippets (i.e., corpus). Scalability, short response time, and the ability to rank result sets among the major challenges have to be dealt with by a clone search engine. In this chapter, we identify and define major concepts related to clone search. We then present a framework that summarizes the architecture of a clone search engine and enables us to provide a systematic view of the internals of such an engine. Finally, we discuss how to benchmark and evaluate the performance of clone search engines. The discussion includes a set of measures that are helpful in evaluating clone search engines.

Source Code Clone Search.

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