:: Library Catalog

Buchumschlag

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Boas, Marcelo Vilas, Pinto, Gustavo, Monteiro, Edward Roberto, Carida, Vinicius Fernandes, Ribeiro, Danilo
Format:	Preprint
Veröffentlicht:	2026
Schlagworte:	Software Engineering
Online-Zugang:	https://arxiv.org/abs/2605.18461
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

It's Not About Whom You Train: An Analysis of Corporate Education in Software Engineering
von: Siqueira, Rodrigo, et al.
Veröffentlicht: (2026)

Understanding the relationships between the perceptions of burnout and instability in Software Engineering
von: Ribeiro, Danilo Monteiro
Veröffentlicht: (2025)

The professional's opinion: Suggestions for improving the corporate education training process in Software Engineering
von: Siqueira, Rodrigo, et al.
Veröffentlicht: (2026)

Fuzzing with Agents? Generators Are All You Need
von: Vikram, Vasudev, et al.
Veröffentlicht: (2026)

AI Advocate: Educational Path to Transform Squads to the Future
von: Soares, Carla, et al.
Veröffentlicht: (2026)

Toward Effective AI Governance: A Review of Principles
von: Ribeiro, Danilo, et al.
Veröffentlicht: (2025)

An Actionable Framework for Understanding and Improving Talent Retention as a Competitive Advantage in IT Organizations
von: Costa, Luiz Alexandre, et al.
Veröffentlicht: (2024)

Affirmative Hackathon for Software Developers with Disabilities: An Industry Initiative
von: Rocha, Thayssa, et al.
Veröffentlicht: (2025)

Modeling Code: Is Text All You Need?
von: Nichols, Daniel, et al.
Veröffentlicht: (2025)

One Documentation Does Not Fit All: Case Study of TensorFlow Documentation
von: Thirimanne, Sharuka Promodya, et al.
Veröffentlicht: (2025)

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

Physics Is All You Need? A Case Study in Physicist-Supervised AI Development of Scientific Software
von: Nguyen, Nhat-Minh
Veröffentlicht: (2026)

Beyond Accuracy: LLM Variability in Evidence Screening for Software Engineering SLRs
von: Hida, Gilberto Sussumu, et al.
Veröffentlicht: (2026)

Intention is All You Need: Refining Your Code from Your Intention
von: Guo, Qi, et al.
Veröffentlicht: (2025)

Names Are All You Need: Effective and Safe Regression Test Selection for Python
von: Wang, You, et al.
Veröffentlicht: (2026)

The Effects of GitHub Copilot on Computing Students' Programming Effectiveness, Efficiency, and Processes in Brownfield Programming Tasks
von: Shihab, Md Istiak Hossain, et al.
Veröffentlicht: (2025)

Why AI Agents Still Need You: Findings from Developer-Agent Collaborations in the Wild
von: Kumar, Aayush, et al.
Veröffentlicht: (2025)

Prompting Is All You Need: Automated Android Bug Replay with Large Language Models
von: Feng, Sidong, et al.
Veröffentlicht: (2023)

Is Reuse All You Need? A Systematic Comparison of Regular Expression Composition Strategies
von: Çakar, Berk, et al.
Veröffentlicht: (2025)

Code Comprehension with GitHub Copilot: Performance Gains, Comprehension Trade-offs, and Behavioral Predictors in Brownfield Programming
von: Qiao, Yunhan, et al.
Veröffentlicht: (2025)

Lessons from Building StackSpot AI: A Contextualized AI Coding Assistant
von: Pinto, Gustavo, et al.
Veröffentlicht: (2023)

The App Squad: SLJ's Advisors Weigh in on Kids' Book Apps
von: Ishizuka, Kathy
Veröffentlicht: (2011)

Assisting Novice Developers Learning in Flutter Through Cognitive-Driven Development
von: Ferreira, Ronivaldo, et al.
Veröffentlicht: (2024)

Requirements are All You Need: The Final Frontier for End-User Software Engineering
von: Robinson, Diana, et al.
Veröffentlicht: (2024)

LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models
von: Huang, Linghan, et al.
Veröffentlicht: (2025)

Mitigating Configuration Differences Between Development and Production Environments: A Catalog of Strategies
von: Nazario, Marcos, et al.
Veröffentlicht: (2025)

AdNanny: One Reasoning LLM for All Offline Ads Recommendation Tasks
von: Hu, Nan, et al.
Veröffentlicht: (2026)

A Mapping Study About Training in Industry Context in Software Engineering
von: de Andrade, Breno Alves, et al.
Veröffentlicht: (2025)

Corporate Training in Brazilian Software Engineering: A Quantitative Study of Professional Perceptions
von: Siqueira, Rodrigo, et al.
Veröffentlicht: (2026)

Are Prompts All You Need? Evaluating Prompt-Based Large Language Models (LLM)s for Software Requirements Classification
von: Binkhonain, Manal, et al.
Veröffentlicht: (2025)

ViC: Virtual Compiler Is All You Need For Assembly Code Search
von: Gao, Zeyu, et al.
Veröffentlicht: (2024)

Rubric Is All You Need: Enhancing LLM-based Code Evaluation With Question-Specific Rubrics
von: Pathak, Aditya, et al.
Veröffentlicht: (2025)

One-for-All Does Not Work! Enhancing Vulnerability Detection by Mixture-of-Experts (MoE)
von: Yang, Xu, et al.
Veröffentlicht: (2025)

Corporate Training in Brazilian Software Engineering: A Qualitative Study of Useful Learning Experiences
von: Siqueira, Rodrigo, et al.
Veröffentlicht: (2026)

Challenges and Experiences of Iranian Developers with MLOps at Enterprise
von: Heydari, Mohammad, et al.
Veröffentlicht: (2024)

Repair Ingredients Are All You Need: Improving Large Language Model-Based Program Repair via Repair Ingredients Search
von: Zhang, Jiayi, et al.
Veröffentlicht: (2025)

Not One to Rule Them All: Mining Meaningful Code Review Orders From GitHub
von: Bouraffa, Abir, et al.
Veröffentlicht: (2025)

One Size Does Not Fit All: Investigating Efficacy of Perplexity in Detecting LLM-Generated Code
von: Xu, Jinwei, et al.
Veröffentlicht: (2024)

MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms
von: Zhou, Youran, et al.
Veröffentlicht: (2025)

Enterprise Identity Integration for AI-Assisted Developer Services: Architecture, Implementation, and Case Study
von: Chinthareddy, Manideep Reddy
Veröffentlicht: (2026)