:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Cheung, Kuen Sum, Kaul, Mayuri, Jahangirova, Gunel, Mousavi, Mohammad Reza, Zie, Eric
Format:	Preprint
Published:	2025
Subjects:	Software Engineering
Online Access:	https://arxiv.org/abs/2505.04521
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Taxonomy of Real Faults in Hybrid Quantum-Classical Architectures
by: Bensoussan, Avner, et al.
Published: (2025)

How Does Chunking Affect Retrieval-Augmented Code Completion? A Controlled Empirical Study
by: Wu, Xinjian, et al.
Published: (2026)

Understanding LLM-Driven Test Oracle Generation
by: Bodicoat, Adam, et al.
Published: (2026)

MuFF: Stable and Sensitive Post-training Mutation Testing for Deep Learning
by: Kim, Jinhan, et al.
Published: (2025)

New Formulation of DNN Statistical Mutation Killing for Ensuring Monotonicity: A Technical Report
by: Kim, Jinhan, et al.
Published: (2025)

Fault Localisation and Repair for DL Systems: An Empirical Study with LLMs
by: Kim, Jinhan, et al.
Published: (2025)

GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming
by: Ayerdi, Jon, et al.
Published: (2023)

Revisiting "Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion": A Critical Review and Implications on DNN Coverage Testing
by: Kim, Jinhan, et al.
Published: (2026)

Benchmarking and Evaluating VLMs for Software Architecture Diagram Understanding
by: Ouyang, Shuyin, et al.
Published: (2026)

muPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults
by: Thomas, Deepak-George, et al.
Published: (2024)

LLMLOOP: Improving LLM-Generated Code and Tests through Automated Iterative Feedback Loops
by: Ravi, Ravin, et al.
Published: (2026)

Real Faults in Deep Learning Fault Benchmarks: How Real Are They?
by: Jahangirova, Gunel, et al.
Published: (2024)

An Empirical Study of Fault Localisation Techniques for Deep Learning
by: Humbatova, Nargiz, et al.
Published: (2024)

Carbon Footprint Evaluation of Code Generation through LLM as a Service
by: Vartziotis, Tina, et al.
Published: (2025)

Robust Mutation Analysis of Quantum Programs Under Noise
by: Fortz, Sophie, et al.
Published: (2026)

The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project
by: Gröpler, Robin, et al.
Published: (2025)

QuCheck: A Property-based Testing Framework for Quantum Programs in Qiskit
by: Pontolillo, Gabriel, et al.
Published: (2025)

Comparing Developer and LLM Biases in Code Evaluation
by: Mittal, Aditya, et al.
Published: (2026)

From Requirements to Code: Understanding Developer Practices in LLM-Assisted Software Engineering
by: Ullrich, Jonathan, et al.
Published: (2025)

Complete FSM Testing Using Strong Separability
by: Hierons, Robert M., et al.
Published: (2025)

Evaluating Efficiency and Novelty of LLM-Generated Code for Graph Analysis
by: Nia, Atieh Barati, et al.
Published: (2025)

Software is infrastructure: failures, successes, costs, and the case for formal verification
by: Bernardi, Giovanni, et al.
Published: (2025)

Code vs Serialized AST Inputs for LLM-Based Code Summarization: An Empirical Study
by: Dong, Shijia, et al.
Published: (2026)

LLM Assisted Coding with Metamorphic Specification Mutation Agent
by: Akhond, Mostafijur Rahman, et al.
Published: (2025)

The Energy Footprint of LLM-Based Environmental Analysis: LLMs and Domain Products
by: Bao, Alicia, et al.
Published: (2026)

RevMine: An LLM-Assisted Tool for Code Review Mining and Analysis Across Git Platforms
by: Kansab, Samah, et al.
Published: (2025)

Pre-Filtering Code Suggestions using Developer Behavioral Telemetry to Optimize LLM-Assisted Programming
by: Awad, Mohammad Nour Al, et al.
Published: (2025)

GeoJSON Agents:A Multi-Agent LLM Architecture for Geospatial Analysis-Function Calling vs Code Generation
by: Luo, Qianqian, et al.
Published: (2025)

Ten Simple Rules for AI-Assisted Coding in Science
by: Bridgeford, Eric W., et al.
Published: (2025)

On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practices
by: Kashif, Syed Mohammad, et al.
Published: (2025)

Engagement in Code Review: Emotional, Behavioral, and Cognitive Dimensions in Peer vs. LLM Interactions
by: Alami, Adam, et al.
Published: (2025)

When LLMs Meet API Documentation: Can Retrieval Augmentation Aid Code Generation Just as It Helps Developers?
by: Chen, Jingyi, et al.
Published: (2025)

LLM Security Guard for Code
by: Kavian, Arya, et al.
Published: (2024)

On the Road to Personalized Code Intelligence: Portraiting and Assisting Developers Based on Their In-IDE Behaviors
by: Liu, Yuhong, et al.
Published: (2026)

Human or LLM? A Comparative Study on Accessible Code Generation Capability
by: Suh, Hyunjae, et al.
Published: (2025)

Security of LLM-generated Code: A Comparative Analysis
by: Morkonda, Srivathsan G, et al.
Published: (2026)

LLM-Assisted Thematic Analysis: Opportunities, Limitations, and Recommendations
by: Ornelas, Tatiane, et al.
Published: (2025)

Review of Tools for Zero-Code LLM Based Application Development
by: Pattnayak, Priyaranjan, et al.
Published: (2025)

Evaluating LLM-Generated Code: A Benchmark and Developer Study
by: Szych, Joanna, et al.
Published: (2026)

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