Cover Image

Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mojica-Hanke, Anamaria, Palacio, David Nader, Poshyvanyk, Denys, Linares-Vásquez, Mario, Herbold, Steffen
Format:	Preprint
Published:	2024
Subjects:	Software Engineering Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An Exploratory Study of Bug-Introducing Changes: Exploring Relationships in Bug-Introducing Changes Towards Causal Understanding
by: Schulte, Lukas, et al.
Published: (2023)

Towards Enabling An Artificial Self-Construction Software Life-cycle via Autopoietic Architectures
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2026)

Legal Aspects for Software Developers Interested in Generative AI Applications
by: Herbold, Steffen, et al.
Published: (2024)

Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives
by: Khati, Dipin, et al.
Published: (2025)

A Path Less Traveled: Reimagining Software Engineering Automation via a Neurosymbolic Paradigm
by: Mastropaolo, Antonio, et al.
Published: (2025)

Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?
by: O'Brien, Conor, et al.
Published: (2024)

SELU: A Software Engineering Language Understanding Benchmark
by: Peña, Fabian C., et al.
Published: (2025)

Rethinking Software Empirical Studies with Structural Causal Models
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2026)

Challenges and Practices in Quantum Software Testing and Debugging: Insights from Practitioners
by: Zappin, Jake, et al.
Published: (2025)

Bridging the Quantum Divide: Aligning Academic and Industry Goals in Software Engineering
by: Zappin, Jake, et al.
Published: (2025)

On Interpreting the Effectiveness of Unsupervised Software Traceability with Information Theory
by: Palacio, David N., et al.
Published: (2024)

Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code?
by: Velasco, Alejandro, et al.
Published: (2024)

Prompting in Practice: Investigating Software Practitioners' Use of Generative AI Tools
by: Otten, Daniel, et al.
Published: (2025)

Developers' Perspectives on Software Licensing: Current Practices, Challenges, and Tools
by: Wintersgill, Nathan, et al.
Published: (2025)

Peer Code Review in Research Software Development: The Research Software Engineer Perspective
by: Malik, Md Ariful Islam, et al.
Published: (2025)

A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code
by: Velasco, Alejandro, et al.
Published: (2025)

Action Research with Industrial Software Engineering -- An Educational Perspective
by: Dittrich, Yvonne, et al.
Published: (2024)

"Don't Be Afraid, Just Learn": Insights from Industry Practitioners to Prepare Software Engineers in the Age of Generative AI
by: Otten, Daniel, et al.
Published: (2026)

Testing Practices, Challenges, and Developer Perspectives in Open-Source IoT Platforms
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2025)

Triage in Software Engineering: A Systematic Review of Research and Practice
by: Zhao, Yongxin, et al.
Published: (2025)

Neurosymbolic Architectural Reasoning: Towards Formal Analysis through Neural Software Architecture Inference
by: Herbold, Steffen, et al.
Published: (2025)

Future of Software Engineering Research: The SIGSOFT Perspective
by: Di Penta, Massimiliano, et al.
Published: (2026)

Toward Neurosymbolic Program Comprehension
by: Velasco, Alejandro, et al.
Published: (2025)

"The Law Doesn't Work Like a Computer": Exploring Software Licensing Issues Faced by Legal Practitioners
by: Wintersgill, Nathan, et al.
Published: (2024)

Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification
by: Peña, Fabian C., et al.
Published: (2025)

Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2026)

Toward Explaining Large Language Models in Software Engineering Tasks
by: Vitale, Antonio, et al.
Published: (2025)

Do Research Software Engineers and Software Engineering Researchers Speak the Same Language?
by: Kehrer, Timo, et al.
Published: (2025)

Utilizing LLMs for Industrial Process Automation: A Case Study on Modifying RAPID Programs
by: Fares, Salim, et al.
Published: (2025)

A Software Engineering Perspective on Testing Large Language Models: Research, Practice, Tools and Benchmarks
by: Hudson, Sinclair, et al.
Published: (2024)

Teaching Literature Reviewing for Software Engineering Research
by: Baltes, Sebastian, et al.
Published: (2024)

When Quantum Meets Classical: Characterizing Hybrid Quantum-Classical Issues Discussed in Developer Forums
by: Zappin, Jake, et al.
Published: (2024)

On the Generalizability of Transformer Models to Code Completions of Different Lengths
by: Cooper, Nathan, et al.
Published: (2025)

Towards More Trustworthy Deep Code Models by Enabling Out-of-Distribution Detection
by: Yan, Yanfu, et al.
Published: (2025)

"False negative -- that one is going to kill you": Understanding Industry Perspectives of Static Analysis based Security Testing
by: Ami, Amit Seal, et al.
Published: (2023)

Causal Inference for the Effect of Code Coverage on Bug Introduction
by: Schulte, Lukas, et al.
Published: (2026)

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study
by: Velasco, Alejandro, et al.
Published: (2024)

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering
by: Palacio, David N.
Published: (2025)

BOMs Away! Inside the Minds of Stakeholders: A Comprehensive Study of Bills of Materials for Software Systems
by: Stalnaker, Trevor, et al.
Published: (2023)

Quantum Software Engineering and Potential of Quantum Computing in Software Engineering Research: A Review
by: Mandal, Ashis Kumar, et al.
Published: (2025)