:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lu, Zeyu, Zhang, Peng, Chong, Chun Yong, Gao, Shan, Yang, Yibiao, Li, Yanhui, Chen, Lin, Zhou, Yuming
Format:	Preprint
Published:	2025
Subjects:	Software Engineering
Online Access:	https://arxiv.org/abs/2511.11999
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Once4All: Skeleton-Guided SMT Solver Fuzzing with LLM-Synthesized Generators
by: Sun, Maolin, et al.
Published: (2025)

ClozeMaster: Fuzzing Rust Compiler by Harnessing LLMs for Infilling Masked Real Programs
by: Gao, Hongyan, et al.
Published: (2026)

Using a Sledgehammer to Crack a Nut? Revisiting Automated Compiler Fault Isolation
by: Yang, Yibiao, et al.
Published: (2025)

DebugHarness: Emulating Human Dynamic Debugging for Autonomous Program Repair
by: Sun, Maolin, et al.
Published: (2026)

Isolating Compiler Faults via Multiple Pairs of Adversarial Compilation Configurations
by: Li, Qingyang, et al.
Published: (2025)

Can Adjusting Hyperparameters Lead to Green Deep Learning: An Empirical Study on Correlations between Hyperparameters and Energy Consumption of Deep Learning Models
by: Wang, Taoran, et al.
Published: (2026)

A Systematic Evaluation of Large Code Models in API Suggestion: When, Which, and How
by: Wang, Chaozheng, et al.
Published: (2024)

ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code
by: Feng, Jia, et al.
Published: (2024)

Hybrid mutation driven testing for natural language inference
by: Linghan Meng, et al.
Published: (2024)

Unveiling the impact of unchanged modules across versions on the evaluation of within‐project defect prediction models
by: Xutong Liu, et al.
Published: (2024)

Towards Better Code Understanding in Decoder-Only Models with Contrastive Learning
by: Lin, Jiayi, et al.
Published: (2024)

When Model Editing Meets Service Evolution: A Knowledge-Update Perspective for Service Recommendation
by: Fan, Guodong, et al.
Published: (2026)

The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for Test Case Generation
by: Gao, Shuzheng, et al.
Published: (2025)

Deep Learning-based Software Engineering: Progress, Challenges, and Opportunities
by: Chen, Xiangping, et al.
Published: (2024)

MulVul: Retrieval-augmented Multi-Agent Code Vulnerability Detection via Cross-Model Prompt Evolution
by: Wu, Zihan, et al.
Published: (2026)

TriFusion-LLM: Prior-Guided Multimodal Fusion with LLM Arbitration for Fine-grained Code Clone Detection
by: Li, Mengdi, et al.
Published: (2026)

Latent Mutants: A large-scale study on the Interplay between mutation testing and software evolution
by: Sohn, Jeongju, et al.
Published: (2025)

A Roadmap on Modern Code Review: Challenges and Opportunities
by: Yang, Zezhou, et al.
Published: (2024)

A Systematic Literature Review of Code Hallucinations in LLMs: Characterization, Mitigation Methods, Challenges, and Future Directions for Reliable AI
by: Gao, Cuiyun, et al.
Published: (2025)

Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering
by: Wang, Ruiqi, et al.
Published: (2025)

AXIOM: Benchmarking LLM-as-a-Judge for Code via Rule-Based Perturbation and Multisource Quality Calibration
by: Wang, Ruiqi, et al.
Published: (2025)

Code Digital Twin: Empowering LLMs with Tacit Knowledge for Complex Software Development
by: Peng, Xin, et al.
Published: (2025)

Code Digital Twin: A Knowledge Infrastructure for AI-Assisted Complex Software Development
by: Peng, Xin, et al.
Published: (2025)

An approach for performance requirements verification and test environments generation
by: Abdeen, Waleed, et al.
Published: (2024)

When Elo Lies: Hidden Biases in Codeforces-Based Evaluation of Large Language Models
by: Zheng, Shenyu, et al.
Published: (2026)

An approach for API synthesis using large language models
by: Zhong, Hua, et al.
Published: (2025)

Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education
by: Pan, Wei Hung, et al.
Published: (2024)

Can LLMs be Effective Code Contributors? A Study on Open-source Projects
by: Chong, Chun Jie, et al.
Published: (2026)

Boosting Static Resource Leak Detection via LLM-based Resource-Oriented Intention Inference
by: Wang, Chong, et al.
Published: (2023)

No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT
by: Liu, Zhijie, et al.
Published: (2023)

Coverage Goal Selector for Combining Multiple Criteria in Search-Based Unit Test Generation
by: Zhou, Zhichao, et al.
Published: (2023)

Risk-based test framework for LLM features in regulated software
by: Zhou, Zhiyin
Published: (2026)

Extracting Conceptual Knowledge to Locate Software Issues
by: Wang, Ying, et al.
Published: (2025)

Contextual Code Retrieval for Commit Message Generation: A Preliminary Study
by: Xiong, Bo, et al.
Published: (2025)

Using Large Language Models for Commit Message Generation: A Preliminary Study
by: Zhang, Linghao, et al.
Published: (2024)

Assessing UML Diagrams by GPT: Implications for Education
by: Wang, Chong, et al.
Published: (2024)

RAG-Enhanced Commit Message Generation
by: Zhang, Linghao, et al.
Published: (2024)

Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision
by: Lin, Hong Yi, et al.
Published: (2026)

V2E: Validating Smart Contract Vulnerabilities through Profit-driven Exploit Generation and Execution
by: Zhang, Jingwen, et al.
Published: (2026)

An empirical study of LoRA-based fine-tuning of large language models for automated test case generation
by: Moradi, Milad, et al.
Published: (2026)