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Found 25 papers, 15 papers with code
SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugs
Large language models (LLMs) are transforming automated program repair (APR) through agent-based approaches that localize bugs, generate patches, and verify fixes.
HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale
Large Language Models (LLMs) have revolutionized software engineering (SE), showcasing remarkable proficiency in various coding tasks.
Dopamin: Transformer-based Comment Classifiers through Domain Post-Training and Multi-level Layer Aggregation
Code comments provide important information for understanding the source code.
XMainframe: A Large Language Model for Mainframe Modernization
To this end, we introduce XMainframe, a state-of-the-art large language model (LLM) specifically designed with knowledge of mainframe legacy systems and COBOL codebases.
On the Impacts of Contexts on Repository-Level Code Generation
CodeLLMs have gained widespread adoption for code generation tasks, yet their capacity to handle repository-level code generation with complex contextual dependencies remains underexplored.
AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology
Software agents have emerged as promising tools for addressing complex software engineering tasks.
Envisioning the Next-Generation AI Coding Assistants: Insights & Proposals
As a research-product hybrid group in AI for Software Engineering (AI4SE), we present four key takeaways from our experience developing in-IDE AI coding assistants.
RepoHyper: Search-Expand-Refine on Semantic Graphs for Repository-Level Code Completion
Furthermore, RepoHyper leverages Expand and Refine retrieval method, including a graph expansion and a link prediction algorithm applied to the RSG, enabling the effective retrieval and prioritization of relevant code snippets.
Functional Overlap Reranking for Neural Code Generation
We introduce SRank, a novel reranking strategy for selecting the best solutions from code generation, focusing on modeling the relationships between clusters of solutions.
CodeTF: One-stop Transformer Library for State-of-the-art Code LLM
In this paper, we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence.
CodeT5+: Open Code Large Language Models for Code Understanding and Generation
To address these limitations, we propose ``CodeT5+'', a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks.
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The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
We present The Vault, a dataset of high-quality code-text pairs in multiple programming languages for training large language models to understand and generate code.
Class based Influence Functions for Error Detection
Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets.
Better Language Models of Code through Self-Improvement
We aim to improve this issue by proposing a simple data augmentation framework.
Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5
Specifically, we propose three objectives to adapt the generic CodeT5 for debugging: a bug detection objective to determine whether a given code snippet is buggy or not, a bug localization objective to identify the buggy lines, and a program repair objective to translate the buggy code to its fixed version.
HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations
We propose a novel method for code summarization utilizing Heterogeneous Code Representations (HCRs) and our specially designed HierarchyNet.
Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code Corpora
Despite the recent trend of developing and applying neural source code models to software engineering tasks, the quality of such models is insufficient for real-world use.
Energy-bounded Learning for Robust Models of Code
In programming, learning code representations has a variety of applications, including code classification, code search, comment generation, bug prediction, and so on.
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.
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.
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.
