Image de couverture de livre

Is Reuse All You Need? A Systematic Comparison of Regular Expression Composition Strategies

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Çakar, Berk, Sale, Charles M., Chen, Sophie, Lee, Dongyoon, Davis, James C.
Format:	Preprint
Publié:	2025
Sujets:	Software Engineering
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Towards the Systematic Testing of Regular Expression Engines
par: Çakar, Berk, et autres
Publié: (2026)

SoK: A Literature and Engineering Review of Regular Expression Denial of Service (ReDoS)
par: Bhuiyan, Masudul Hasan Masud, et autres
Publié: (2024)

ConfuGuard: Using Metadata to Detect Active and Stealthy Package Confusion Attacks Accurately and at Scale
par: Jiang, Wenxin, et autres
Publié: (2025)

Towards a Benchmark for Dependency Decision-Making
par: Singla, Tanmay, et autres
Publié: (2026)

Evaluating the Impact of Data Cleaning on the Quality of Generated Pull Request Descriptions
par: Tire, Kutay, et autres
Publié: (2025)

PR-Aware Automated Unit Test Generation: Challenges and Opportunities
par: Haratian, Vahid, et autres
Publié: (2026)

Fuzzing with Agents? Generators Are All You Need
par: Vikram, Vasudev, et autres
Publié: (2026)

Requirements are All You Need: From Requirements to Code with LLMs
par: Wei, Bingyang
Publié: (2024)

Prompting Is All You Need: Automated Android Bug Replay with Large Language Models
par: Feng, Sidong, et autres
Publié: (2023)

Modeling Code: Is Text All You Need?
par: Nichols, Daniel, et autres
Publié: (2025)

Intention is All You Need: Refining Your Code from Your Intention
par: Guo, Qi, et autres
Publié: (2025)

Names Are All You Need: Effective and Safe Regression Test Selection for Python
par: Wang, You, et autres
Publié: (2026)

Reusing Deep Learning Models: Challenges and Directions in Software Engineering
par: Davis, James C., et autres
Publié: (2024)

LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models
par: Huang, Linghan, et autres
Publié: (2025)

Repair Ingredients Are All You Need: Improving Large Language Model-Based Program Repair via Repair Ingredients Search
par: Zhang, Jiayi, et autres
Publié: (2025)

Are Prompts All You Need? Evaluating Prompt-Based Large Language Models (LLM)s for Software Requirements Classification
par: Binkhonain, Manal, et autres
Publié: (2025)

One Developer Is All You Need: A Case Study of an AI-Augmented One-Person Squad in a Brownfield Enterprise
par: Boas, Marcelo Vilas, et autres
Publié: (2026)

Drop the Golden Apples: Identifying Third-Party Reuse by DB-Less Software Composition Analysis
par: Zhang, Lyuye, et autres
Publié: (2025)

Software Dependencies 2.0: An Empirical Study of Reuse and Integration of Pre-Trained Models in Open-Source Projects
par: Yasmin, Jerin, et autres
Publié: (2025)

SlsReuse: LLM-Powered Serverless Function Reuse
par: Wen, Jinfeng, et autres
Publié: (2025)

An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process
par: Synovic, Nicholas M., et autres
Publié: (2026)

Rubric Is All You Need: Enhancing LLM-based Code Evaluation With Question-Specific Rubrics
par: Pathak, Aditya, et autres
Publié: (2025)

Requirements are All You Need: The Final Frontier for End-User Software Engineering
par: Robinson, Diana, et autres
Publié: (2024)

Regular Expression Denial of Service Induced by Backreferences
par: Liu, Yichen, et autres
Publié: (2026)

What You Need is What You Get: Theory of Mind for an LLM-Based Code Understanding Assistant
par: Richards, Jonan, et autres
Publié: (2024)

ViC: Virtual Compiler Is All You Need For Assembly Code Search
par: Gao, Zeyu, et autres
Publié: (2024)

On the Need for Configurable Travel Recommender Systems: A Systematic Mapping Study
par: Pereira, Rickson Simioni, et autres
Publié: (2024)

A Guide to Stakeholder Analysis for Cybersecurity Researchers
par: Davis, James C, et autres
Publié: (2025)

Investigating Code Reuse in Software Redesign: A Case Study
par: Zhang, Xiaowen, et autres
Publié: (2026)

How Do Java Developers Reuse StackOverflow Answers in Their GitHub Projects?
par: Chen, Juntong, et autres
Publié: (2023)

AutoMerge: Search-Based Model Merging Framework for Effective Model Reuse
par: Lu, You, et autres
Publié: (2026)

Studying the Role of Reusing Crowdsourcing Knowledge in Software Development
par: Abdalkareem, Rabe
Publié: (2025)

Skill-Adpative Imitation Learning for UI Test Reuse
par: Wu, Mengzhou, et autres
Publié: (2024)

Dataset: Copy-based Reuse in Open Source Software
par: Jahanshahi, Mahmoud, et autres
Publié: (2023)

ReSyn: A Generalized Recursive Regular Expression Synthesis Framework
par: Kim, Seongmin, et autres
Publié: (2026)

Copy-and-Paste? Identifying EVM-Inequivalent Code Smells in Multi-chain Reuse Contracts
par: Wang, Zexu, et autres
Publié: (2025)

Automation and Reuse Practices in GitHub Actions Workflows: A Practitioner's Perspective
par: Delicheh, Hassan Onsori, et autres
Publié: (2026)

On the Future of Software Reuse in the Era of AI Native Software Engineering
par: Taivalsaari, Antero, et autres
Publié: (2025)

Why Software Signing (Still) Matters: Trust Boundaries in the Software Supply Chain
par: Kalu, Kelechi G., et autres
Publié: (2025)

On the Contents and Utility of IoT Cybersecurity Guidelines
par: Chen, Jesse, et autres
Publié: (2023)