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Found 15 papers, 8 papers with code
Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection
However, a challenge in deploying deep learning for vulnerability detection is the limited availability of training data.
MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities
Moreover, it develops a multi-metapath heterogeneous graph attention network to learn multi-level embeddings of different types of nodes and their metapaths in the heterogeneous contract graphs, which can capture the code semantics of smart contracts more accurately and facilitate both fine-grained line-level and coarse-grained contract-level vulnerability detection.
Investigating Math Word Problems using Pretrained Multilingual Language Models
In this paper, we revisit math word problems~(MWPs) from the cross-lingual and multilingual perspective.
AndroEvolve: Automated Update for Android Deprecated-API Usages
Usages of deprecated APIs in Android apps need to be updated to ensure the apps' compatibility with the old and new versions of Android OS.
Software Engineering
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees
We trained an InferCode model instance using the Tree-based CNN as the encoder of a large set of Java code and applied it to downstream unsupervised tasks such as code clustering, code clone detection, cross-language code search or reused under a transfer learning scheme to continue training the model weights for supervised tasks such as code classification and method name prediction.
Characterization and Automatic Update of Deprecated Machine-Learning API Usages
In this paper, we built a tool to automate these updates.
Software Engineering
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations
Corder is designed to alleviate the need of labeled data for code retrieval and code summarization tasks.
TreeCaps: Tree-Based Capsule Networks for Source Code Processing
Although syntax trees are precisely defined according to the language grammar and easier to construct and process than graphs, previous tree-based learning techniques have not been able to learn semantic information from trees to achieve better accuracy than graph-based techniques.
On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations
With the prevalence of publicly available source code repositories to train deep neural network models, neural program models can do well in source code analysis tasks such as predicting method names in given programs that cannot be easily done by traditional program analysis techniques.
Checking Smart Contracts with Structural Code Embedding
However, many bugs and vulnerabilities have been identified in many contracts which raises serious concerns about smart contract security, not to mention that the blockchain systems on which the smart contracts are built can be buggy.
Software Engineering
TreeCaps: Tree-Structured Capsule Networks for Program Source Code Processing
Program comprehension is a fundamental task in software development and maintenance processes.
SmartEmbed: A Tool for Clone and Bug Detection in Smart Contracts through Structural Code Embedding
In addition to the uses by individual developers, SmartEmbed can also be applied to studies of smart contracts in a large scale.
Software Engineering
SAR: Learning Cross-Language API Mappings with Little Knowledge
However, all these approaches still require large amount of manual effort in preparing parallel program corpora, ranging from pairs of APIs, to manually identified code in different languages that are considered as functionally equivalent.
Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code
Our preliminary evaluations on about 40, 000 Java and C# source files from 9 software projects show that our approach can automatically learn shared embeddings for various code elements in different languages and identify their cross-language mappings with reasonable Mean Average Precision scores.
Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks
It is layered on top of two tree-based convolutional neural networks (TBCNNs), each of which recognizes the algorithm of code written in an individual programming language.
