Papers Method name prediction
“Method name prediction” 태그가 달린 논문 25편 · 필터 해제
Analysis of AdvFusion: Adapter-based Multilingual Learning for Code Large Language Models
Programming languages can benefit from one another by utilizing a language model for software engineering tasks. Full fine-tuning and Parameter Efficient Fine-Tuning (PEFT) of Code Language Models (Code-LMs) has been exp…
Method name predictionCode TranslationCode GenerationDo Current Language Models Support Code Intelligence for R Programming Language?
Recent advancements in developing Pre-trained Language Models for Code (Code-PLMs) have urged many areas of Software Engineering (SE) and brought breakthrough results for many SE tasks. Though these models have achieved …
Code SummarizationMethod name predictionAn Empirical Study on Capability of Large Language Models in Understanding Code Semantics
Large Language Models for Code (code LLMs) have demonstrated remarkable performance across various software engineering (SE) tasks, increasing the application of code LLMs in software development. Despite the success of …
Code SummarizationMethod name predictionSensitivityWRDScore: New Metric for Evaluation of Natural Language Generation Models
Evaluating natural language generation models, particularly for method name prediction, poses significant challenges. A robust metric must account for the versatility of method naming, considering both semantic and synta…
Method name predictionText GenerationStudying Vulnerable Code Entities in R
Pre-trained Code Language Models (Code-PLMs) have shown many advancements and achieved state-of-the-art results for many software engineering tasks in the past few years. These models are mainly targeted for popular prog…
Code SummarizationMethod name predictionTransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation
Artificial intelligence (AI) has revolutionized software engineering (SE) by enhancing software development efficiency. The advent of pre-trained models (PTMs) leveraging transfer learning has significantly advanced AI f…
Clone DetectionCode SearchContrastive LearningData Augmentation+2Evaluating the Impact of Source Code Parsers on ML4SE Models
As researchers and practitioners apply Machine Learning to increasingly more software engineering problems, the approaches they use become more sophisticated. A lot of modern approaches utilize internal code structure in…
Method name predictionAssessing Project-Level Fine-Tuning of ML4SE Models
Machine Learning for Software Engineering (ML4SE) is an actively growing research area that focuses on methods that help programmers in their work. In order to apply the developed methods in practice, they need to achiev…
Method name predictionSyntax-Guided Program Reduction for Understanding Neural Code Intelligence Models
Neural code intelligence (CI) models are opaque black-boxes and offer little insight on the features they use in making predictions. This opacity may lead to distrust in their prediction and hamper their wider adoption i…
Method name predictionExtracting Label-specific Key Input Features for Neural Code Intelligence Models
The code intelligence (CI) models are often black-box and do not offer any insights on the input features that they learn for making correct predictions. This opacity may lead to distrust in their prediction and hamper t…
Method name predictionPredictionCode2Snapshot: Using Code Snapshots for Learning Representations of Source Code
There are several approaches for encoding source code in the input vectors of neural models. These approaches attempt to include various syntactic and semantic features of input programs in their encoding. In this paper,…
Code ClassificationMethod name predictionMemorization and Generalization in Neural Code Intelligence Models
Deep Neural Networks (DNNs) are increasingly being used in software engineering and code intelligence tasks. These are powerful tools that are capable of learning highly generalizable patterns from large datasets through…
Code Documentation GenerationCode SearchMemorizationMethod name prediction+1Understanding Neural Code Intelligence Through Program Simplification
A wide range of code intelligence (CI) tools, powered by deep neural networks, have been developed recently to improve programming productivity and perform program analysis. To reliably use such tools, developers often n…
Method name predictionVariable misusePSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code
The application of machine learning algorithms to source code has grown in the past years. Since these algorithms are quite sensitive to input data, it is not surprising that researchers experiment with input representat…
Method name predictionUniversal Representation for Code
Learning from source code usually requires a large amount of labeled data. Despite the possible scarcity of labeled data, the trained model is highly task-specific and lacks transferability to different tasks. In this wo…
Link PredictionMethod name predictionInferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees
Building deep learning models on source code has found many successful software engineering applications, such as code search, code comment generation, bug detection, code migration, and so on. Current learning technique…
Clone DetectionCode ClassificationCode Comment GenerationCode Search+5Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations
We propose Corder, a self-supervised contrastive learning framework for source code model. Corder is designed to alleviate the need of labeled data for code retrieval and code summarization tasks. The pre-trained model o…
ClusteringCode SummarizationContrastive LearningInformation Retrieval+6Towards Demystifying Dimensions of Source Code Embeddings
Source code representations are key in applying machine learning techniques for processing and analyzing programs. A popular approach in representing source code is neural source code embeddings that represents programs …
Method name predictionOn 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 th…
Method name predictionContrastive Code Representation Learning
Recent work learns contextual representations of source code by reconstructing tokens from their context. For downstream semantic understanding tasks like summarizing code in English, these representations should ideally…
Clone DetectionContrastive LearningData AugmentationMethod name prediction+3