87 citations · 130 across the 27 of their papers we have counts for
27 papers · 1 filter
Efficient SWE Agent Benchmarking via Trajectory-Aware Evaluation
Kefeng Duan, Dewu Zheng, Yanlin Wang +7
Evaluating software engineering agents on realistic benchmarks is costly, since each task may require multi-step code exploration, modification, and test execution. Existing effici…
Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation
Kefeng Duan, Dewu Zheng, Yanlin Wang +8
The repository-level code generation task requires synthesizing code that satisfies task requirements while remaining consistent with the target repository context. Since real-worl…
SWE-Prime: Fewer Trajectories, Better Performance
Dewu Zheng, Ruizhe Ye, Yanlin Wang +7
To improve large language models' ability to resolve real-world software issues, prior work has focused on constructing large-scale agent trajectory datasets and performing supervi…
Yet Even Less Is Even Better For Agentic, Reasoning, and Coding LLMs
CodeArts Model Team, Yang Ye, Jingyuan Tan +24
Training effective software engineering agents requires large volumes of task-specific trajectories, incurring substantial data construction costs. Inspired by the "Less-Is-More" h…
DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning
Yanlin Wang, Jiadong Wu, Tianyue Jiang +7
Large language models (LLMs) have demonstrated impressive capabilities in code generation by leveraging retrieval-augmented generation (RAG) methods. However, the computational cos…
ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generation
Sicong Liu, Yanxian Huang, Mingwei Liu +6
Code generation tasks aim to automate the conversion of user requirements into executable code, significantly reducing manual development efforts and enhancing software productivit…
AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion
Tianyue Jiang, Yanli Wang, Yanlin Wang +5
Repository-level code completion remains a challenging task for existing code large language models (code LLMs) due to their limited understanding of repository-specific context an…
SimpleDevQA: Benchmarking Large Language Models on Development Knowledge QA
Jing Zhang, Lianghong Guo, Yanlin Wang +7
The Development Knowledge Question Answering (Dev Knowledge QA) task aims to provide natural language answers to knowledge-seeking questions during software development. To investi…
From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence
Jian Yang, Xianglong Liu, Weifeng Lv +68
Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, dri…
EffiReasonTrans: RL-Optimized Reasoning for Code Translation
Yanlin Wang, Rongyi Ou, Yanli Wang +6
Code translation is a crucial task in software development and maintenance. While recent advancements in large language models (LLMs) have improved automated code translation accur…
Towards an Understanding of Context Utilization in Code Intelligence
Yanlin Wang, Kefeng Duan, Dewu Zheng +9
Code intelligence is an emerging domain in software engineering, aiming to improve the effectiveness and efficiency of various code-related tasks. Recent research suggests that inc…
SECRET: Towards Scalable and Efficient Code Retrieval via Segmented Deep Hashing
Wenchao Gu, Ensheng Shi, Yanlin Wang +5
Code retrieval, which retrieves code snippets based on users' natural language descriptions, is widely used by developers and plays a pivotal role in real-world software developmen…
Top General Performance = Top Domain Performance? DomainCodeBench: A Multi-domain Code Generation Benchmark
Dewu Zheng, Yanlin Wang, Ensheng Shi +4
With the rapid advancement of large language models (LLMs), extensive research has been conducted to investigate the code generation capabilities of LLMs. However, existing efforts…
Agents in Software Engineering: Survey, Landscape, and Vision
Yanlin Wang, Wanjun Zhong, Yanxian Huang +7
In recent years, Large Language Models (LLMs) have achieved remarkable success and have been widely used in various downstream tasks, especially in the tasks of the software engine…
LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation
Ziyao Zhang, Yanlin Wang, Chong Wang +2
Code generation aims to automatically generate code from input requirements, significantly enhancing development efficiency. Recent large language models (LLMs) based approaches ha…
You Augment Me: Exploring ChatGPT-based Data Augmentation for Semantic Code Search
Yanlin Wang, Lianghong Guo, Ensheng Shi +8
Code search plays a crucial role in software development, enabling developers to retrieve and reuse code using natural language queries. While the performance of code search models…
When to Stop? Towards Efficient Code Generation in LLMs with Excess Token Prevention
Lianghong Guo, Yanlin Wang, Ensheng Shi +6
Code generation aims to automatically generate code snippets that meet given natural language requirements and plays an important role in software development. Although Code LLMs h…
HumanEvo: An Evolution-aware Benchmark for More Realistic Evaluation of Repository-level Code Generation
Dewu Zheng, Yanlin Wang, Ensheng Shi +4
To evaluate the repository-level code generation capabilities of Large Language Models (LLMs) in complex real-world software development scenarios, many evaluation methods have bee…
SoTaNa: The Open-Source Software Development Assistant
Ensheng Shi, Fengji Zhang, Yanlin Wang +6
Software development plays a crucial role in driving innovation and efficiency across modern societies. To meet the demands of this dynamic field, there is a growing need for an ef…
Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond
Ensheng Shi, Yanlin Wang, Hongyu Zhang +4
Recently, fine-tuning pre-trained code models such as CodeBERT on downstream tasks has achieved great success in many software testing and analysis tasks. While effective and preva…
CoCoSoDa: Effective Contrastive Learning for Code Search
Ensheng Shi, Yanlin Wang, Wenchao Gu +5
Code search aims to retrieve semantically relevant code snippets for a given natural language query. Recently, many approaches employing contrastive learning have shown promising r…
RACE: Retrieval-Augmented Commit Message Generation
Ensheng Shi, Yanlin Wang, Wei Tao +5
Commit messages are important for software development and maintenance. Many neural network-based approaches have been proposed and shown promising results on automatic commit mess…
CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees
Ensheng Shi, Yanlin Wang, Lun Du +4
Code summarization aims to generate concise natural language descriptions of source code, which can help improve program comprehension and maintenance. Recent studies show that syn…
On the Evaluation of Commit Message Generation Models: An Experimental Study
Wei Tao, Yanlin Wang, Ensheng Shi +5
Commit messages are natural language descriptions of code changes, which are important for program understanding and maintenance. However, writing commit messages manually is time-…