Fuzzy Graph Neural Network for Few-Shot Learning

Tong, Wei; Hou, Junlin; Feng, Rui

doi:10.1109/ijcnn48605.2020.9207213

Cited by 21 publications

(18 citation statements)

References 19 publications

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“…Code generation models have been applied to a variety of tasks, including test generation [19], docstring generation [20], code search [17,21], type inference [22,23,24], and more [25]. We focus on the natural-language-to-code task (NL2Code): given the description of a function in natural language, complete the function body.…”

Section: The Natural Language To Code Taskmentioning

confidence: 99%

“…Other tasks. Although we focus specifically on benchmarks for the code generation task, there are many other tasks that have been used to evaluate code generation models, including generating unit tests from code [19], code search [17,21], and type inference [22,23,24]. Lu et al [20] propose a suite of evaluation datasets for ten tasks, including code translation, docstring generation, and code summarization.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation

Federico

Gouwar

Nguyen

et al. 2023

IIEEE Trans. Software Eng.

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Large language models have demonstrated the ability to generate both natural language and programming language text. Although contemporary code generation models are trained on corpora with several programming languages, they are tested using benchmarks that are typically monolingual. The most widely used code generation benchmarks only target Python, so there is little quantitative evidence of how code generation models perform on other programming languages. We propose MultiPL-E, a system for translating unit test-driven code generation benchmarks to new languages. We create the first massively multilingual code generation benchmark by using MultiPL-E to translate two popular Python code generation benchmarks to 18 additional programming languages.We use MultiPL-E to extend the HumanEval benchmark [1] and MBPP benchmark [2] to 18 languages that encompass a range of programming paradigms and popularity. Using these new parallel benchmarks, we evaluate the multi-language performance of three state-ofthe-art code generation models: Codex [1], CodeGen [3] and InCoder [4]. We find that Codex matches or even exceeds its performance on Python for several other languages. The range of programming languages represented in MultiPL-E allow us to explore the impact of language frequency and language features on model performance. Finally, the MultiPL-E approach of compiling code generation benchmarks to new programming languages is both scalable and extensible, making it straightforward to evaluate new models, benchmarks, and languages.4. These source-to-source compilers are sometimes called transpilers.

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Section: The Natural Language To Code Taskmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation

Federico

Gouwar

Nguyen

et al. 2023

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

show abstract

“…A GNN learns the current node representation by repeatedly combining the features of neighboring nodes using the message passing algorithm, thereby generating similar representations for strongly linked nodes. Due to the advantages of GNNs, some approaches [13], [24], [25] incorporated GNNs in few-shot learning domain and demonstrated promising results. The authors of [13] used a GNN as a label propagation module to forecast the label of unlabeled nodes.…”

Section: Graph Neural Networkmentioning

confidence: 99%

Few-Shot Learning-Based Malicious IoT Traffic Detection with Prototypical Graph Neural Networks

THEIN,

SHIRAISHI,

MORII

2023

IEICE Trans. Inf. & Syst.

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With a rapidly escalating number of sophisticated cyberattacks, protecting Internet of Things (IoT) networks against unauthorized activity is a major concern. The detection of malicious attack traffic is thus crucial for IoT security to prevent unwanted traffic. However, existing traditional malicious traffic detection systems which relied on supervised machine learning approach need a considerable number of benign and malware traffic samples to train the machine learning models. Moreover, in the cases of zero-day attacks, only a few labeled traffic samples are accessible for analysis. To deal with this, we propose a few-shot malicious IoT traffic detection system with a prototypical graph neural network. The proposed approach does not require prior knowledge of network payload binaries or network traffic signatures. The model is trained on labeled traffic data and tested to evaluate its ability to detect new types of attacks when only a few labeled traffic samples are available. The proposed detection system first categorizes the network traffic as a bidirectional flow and visualizes the binary traffic flow as a color image. A neural network is then applied to the visualized traffic to extract important features. After that, using the proposed few-shot graph neural network approach, the model is trained on different few-shot tasks to generalize it to new unseen attacks. The proposed model is evaluated on a network traffic dataset consisting of benign traffic and traffic corresponding to six types of attacks. The results revealed that our proposed model achieved an F1 score of 0.91 and 0.94 in 5-shot and 10-shot classification, respectively, and outperformed the baseline models.

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“…Wei et al, (2020) developed LambdaNet for inferring types for TypeScript. Given a program, LambdaNet first transforms it to a type dependency graph, where nodes are type variables for subexpressions in the programs and hyperedges express constraints (such as the subtyping relation or type equality).…”

Section: Related Workmentioning

confidence: 99%

Migrating gradual types

Campora¹,

Chen²,

Erwig³

et al. 2022

J. Funct. Prog.

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Gradual typing allows programs to enjoy the benefits of both static typing and dynamic typing. While it is often desirable to migrate a program from more dynamically typed to more statically typed or vice versa, gradual typing itself does not provide a way to facilitate this migration. This places the burden on programmers who have to manually add or remove type annotations. Besides the general challenge of adding type annotations to dynamically typed code, there are subtle interactions between these annotations in gradually typed code that exacerbate the situation. For example, to migrate a program to be as static as possible, in general, all possible combinations of adding or removing type annotations from parameters must be tried out and compared. In this paper, we address this problem by developing migrational typing, which efficiently types all possible ways of replacing dynamic types with fully static types for a gradually typed program. The typing result supports automatically migrating a program to be as static as possible or introducing the least number of dynamic types necessary to remove a type error. The approach can be extended to support user-defined criteria about which annotations to modify. We have implemented migrational typing and evaluated it on large programs. The results show that migrational typing scales linearly with the size of the program and takes only 2–4 times longer than plain gradual typing.

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Fuzzy Graph Neural Network for Few-Shot Learning

Cited by 21 publications

References 19 publications

MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation

MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation

Few-Shot Learning-Based Malicious IoT Traffic Detection with Prototypical Graph Neural Networks

Migrating gradual types

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