A Source Code Similarity Based on Siamese Neural Network

Xie, Chunli; Wang, Xia; Cheng, Qiansheng; Wang, Mengqi

doi:10.3390/app10217519

Cited by 15 publications

(3 citation statements)

References 35 publications

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“…Due to deep learning technology and activation functions, deep metric learning, as a combination of deep learning and metric learning, has provided excellent solutions in many classification tasks and attracted researchers’ attention in academia and industry. In the Humpback Whale Identification competition held on the Kaggle platform, which is the world’s largest data science community [ 49 ], the top five participating teams’ solutions all applied deep metric learning models: Triplet neural network [ 50 ] and siamese neural network [ 51 ]. The most conspicuous characteristic of these networks is the sharing weights, which makes the samples related because the triplet neural network can simultaneously learn both positive and negative distances and the number of training data combinations increases significantly to avoid overfitting.…”

Section: Methodsmentioning

confidence: 99%

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Wang

Chen

et al. 2021

Biosensors

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Premature ventricular contractions (PVCs), common in the general and patient population, are irregular heartbeats that indicate potential heart diseases. Clinically, long-term electrocardiograms (ECG) collected from the wearable device is a non-invasive and inexpensive tool widely used to diagnose PVCs by physicians. However, analyzing these long-term ECG is time-consuming and labor-intensive for cardiologists. Therefore, this paper proposed a simplistic but powerful approach to detect PVC from long-term ECG. The suggested method utilized deep metric learning to extract features, with compact intra-product variance and separated inter-product differences, from the heartbeat. Subsequently, the k-nearest neighbors (KNN) classifier calculated the distance between samples based on these features to detect PVC. Unlike previous systems used to detect PVC, the proposed process can intelligently and automatically extract features by supervised deep metric learning, which can avoid the bias caused by manual feature engineering. As a generally available set of standard test material, the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database is used to evaluate the proposed method, and the experiment takes 99.7% accuracy, 97.45% sensitivity, and 99.87% specificity. The simulation events show that it is reliable to use deep metric learning and KNN for PVC recognition. More importantly, the overall way does not rely on complicated and cumbersome preprocessing.

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Section: Methodsmentioning

confidence: 99%

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Wang

Chen

et al. 2021

Biosensors

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“…They started to be used by Bromley et al [19] in signature verification work. They have also been used for different kinds of problems, such as the evaluation of source code similarity [20], cyber attack detection [21], object tracking [22], chromosome classification [23] and even animal sound classification [24]. More recently and regarding the grocery products, Ciocca et al [25] have applied a Siamese network to capture the relations between iconic and natural images in the Grocery Store Dataset [3].…”

Section: Solving the One-shot Learning Problemmentioning

confidence: 99%

Improvement of One-Shot-Learning by Integrating a Convolutional Neural Network and an Image Descriptor into a Siamese Neural Network

2021

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Over the last few years, several techniques have been developed with the aim of implementing one-shot learning, a concept that allows classifying images with only a single image per training category. Conceptually, these methods seek to reproduce certain behavior that humans have. People are able to recognize a person they have only seen once, but they are probably not able to do the same with certain animals, such as a monkey. This is because our brains have been trained for years with images of people but not so much of animals. Among the one-shot learning techniques, some of them have used data generation, such as Generative Adversarial Networks (GAN). Other techniques have been based on the matching of descriptors traditionally used for object detection. Finally, one of the most prominent techniques involves using Siamese neural networks. Siamese networks are usually implemented with two convolutional nets that share their weights. They receive two images as input and can detect whether they belong to the same category or not. In the field of grocery products, there has been a lot of research on the one-shot learning problem but not so much on the use of Siamese networks. In this paper, several classifiers are firstly evaluated to decide on a convolutional model to be used with the Siamese and to improve the baseline results obtained in the dataset used. Then, two existing techniques are integrated within the Siamese model: a convolutional net and a Local Maximal Occurrence (LOMO) descriptor. The latter was initially used for the re-identification of people although it has shown its effectiveness to improve the values of a traditional Siamese with only convolutional sisters. The whole network is trained on categories and responds to different categories, showing its strong capacity to deal with the problem of having only one image per category.

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“…In some of the recent studies, researchers have highlighted the potential use of information retrieval with learning-based approaches to detect type-4 clones. Xie et al [53] used the TF-IDF approach with the Siamese neural network, while Fu et al [54] combined a Continuous Bag of Words (CBOW)/Skip Gram (SG) model with ensemble learning and reported improved accuracy measures as compared to other solo approaches. Some researchers have used LSI to further examine the clones, which have already been detected by some clone detection tools.…”

Section: Introductionmentioning

confidence: 99%

Identifying High-Level Concept Clones in Software Programs Using Method’s Descriptive Documentation

Gupta

Goyal

2021

Symmetry

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Software clones are code fragments with similar or nearly similar functionality or structures. These clones are introduced in a project either accidentally or deliberately during software development or maintenance process. The presence of clones poses a significant threat to the maintenance of software systems and is on the top of the list of code smell types. Clones can be simple (fine-grained) or high-level (coarse-grained), depending on the chosen granularity of code for the clone detection. Simple clones are generally viewed at the lines/statements level, whereas high-level clones have granularity as a block, method, class, or file. High-level clones are said to be composed of multiple simple clones. This study aims to detect high-level conceptual code clones (having granularity as java methods) in java-based projects, which is extendable to the projects developed in other languages as well. Conceptual code clones are the ones implementing a similar higher-level abstraction such as an Abstract Data Type (ADT) list. Based on the assumption that “similar documentation implies similar methods”, the proposed mechanism uses “documentation” associated with methods to identify method-level concept clones. As complete documentation does not contribute to the method’s semantics, we extracted only the description part of the method’s documentation, which led to two benefits: increased efficiency and reduced text corpus size. Further, we used Latent Semantic Indexing (LSI) with different combinations of weight and similarity measures to identify similar descriptions in the text corpus. To show the efficacy of the proposed approach, we validated it using three java open source systems of sufficient length. The findings suggest that the proposed mechanism can detect methods implementing similar high-level concepts with improved recall values.

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A Source Code Similarity Based on Siamese Neural Network

Cited by 15 publications

References 35 publications

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Improvement of One-Shot-Learning by Integrating a Convolutional Neural Network and an Image Descriptor into a Siamese Neural Network

Identifying High-Level Concept Clones in Software Programs Using Method’s Descriptive Documentation

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