code2vec 是一种将代码转换为向量的技术,通过将源代码映射为连续的向量表示,使得计算机可以更容易地理解和分析代码。code2vec 技术可以为程序员提供一种全新的编程方式,帮助他们更高效地完成编程任务。 二、code2vec 实现原理 code2vec 的实现原理主要基于深度学习技术。它通过训练神经网络,将源代码映射为连续的向量...
Code2Vec has emerged as a powerful tool for analyzing source code by leveraging distributed representations. Code2Vec has demonstrated substantial capabilities in capturing semantic information from source code; however, its sensitivity to variable names has been identified as a significant limitation. ...
python3 code2vec.py --load models/java14_model/saved_model_iter8.release --test data/java14m/java14m.test.c2v While evaluating, a file named "log.txt" is written with each test example name and the model's prediction. Step 4: Manual examination of a trained model ...
The dataset (X,Y)C represents that there are C types of different code similarity relationships. We define that each element in the CSD dataset is a triplet composed of 3 functions, expressed as 〈a,p,n〉, where a, p are any two similarity functions in (X,Y), and n is any ...
This input is defined as {wO,1 , ... , wO,C }, where C is the word window size that you define. For example, the input could be: {"I","drove","my","to","school"} 1.6.2 Outputs The output of the neural network will be wi. Hence you can think of the task as "predictin...
摘要: Code embedding, as an emerging paradigm for source code analysis, has attracted much attention over the past few years. It aims to represent code semantics through distributed vector representation...关键词:Flow2Vec asymmetric transitivity code embedding value-flows ...
输入层: 输入层的节点为C个上下文词语的one-hot表示,共C*V输入节点。 映射层: 将输入层节点乘上权重矩阵WV∗NWV∗N得到的词向量(word embedding)求平均得到h。公式如下。 ht=1C∗WT∑i=1Cxi=1C∗∑i=1Cvi其中C个xi均为词语t的上下文,C的大小可以人为指定ht=1C∗WT∑i=1Cxi=1C∗∑i=1...
The results we obtained in a challenging source code classification task suggest that, compared to code2vec, the RNN-based paths representation can produce a better embedding model with fewer training parameters.doi:10.1016/j.cose.2023.103322Sun X.Liu C.Dong W.Liu T.Computers & Security...
In this work, we elaborate upon a state-of-the-art approach for source code representation, which uses information about its syntactic structure, and we extend it to represent source code changes (i.e., commits). We use this representation to tackle an industrial-relevant task: the ...
Deep learning methods, which have found successful applications in fields\nlike image classification and natural language processing, have recently been\napplied to source code analysis too, due to the enormous amount of freely\navailable source code (e.g., from open-source software repositories)....