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

arXiv:2411.19304 · cs.SE, cs.LG · Submitted 2024-11-28 · Read on arXiv

cs.SE, cs.LG

Submitted: 2024-11-28

Updated: 2026-09-17

Comments: Submitted (May 2025) and accepted (July 2026) in Empirical Software Engineering

Journal ref: Mojica-Hanke, A., Palacio, D.N., Poshyvanyk, D. et al. Perspective of software engineering researchers on machine learning practices regarding research, review, and education. Empir Software Eng 32, 35 (2027)

DOI: 10.1007/s10664-026-10926-z

Code: https://github.com/aieng-lab/Replication-kit-Perspective-of-SE-Researchers-on-ML-Practices

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Software Engineering — 76× in this paper
• Machine Learning — 63× in this paper
• Feature Engineering — 29× in this paper · explained in 3 episodes
• Deep Learning — 19× in this paper
• Embeddings — 11× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Artificial Intelligence — 6× in this paper
• Core — 6× in this paper · explained in 2 episodes
• International Conference on Software Engineering — 6× in this paper
• Interpretability — 6× in this paper · explained in 15 episodes
• Transactions on Software Engineering and Methodology — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Abstraction — 3× in this paper · explained in 1 episode
• Active Learning — 3× in this paper · explained in 6 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Concept Drift — 3× in this paper · explained in 4 episodes
• Feature Selection — 3× in this paper · explained in 4 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Explainability — 2× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Symposium on Empirical Software Engineering and Measurement — 2× in this paper
• Verification — 2× in this paper · explained in 2 episodes
• AI Integration — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Conference on Software Engineering and Advanced Applications — 1× in this paper
• Federated Learning — 1× in this paper · explained in 14 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• IEEE Frontiers in Education Conference — 1× in this paper
• Q-learning — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• What Do Developers Ask About ML Libraries? A Large-scale Study Using Stack Overflow
• What are the Machine Learning best practices reported by practitioners on Stack Exchange?
• Empirical Standards for Software Engineering Research
• Towards Integrating Emerging AI Applications in SE Education
• Teaching Machine Learning in K-12 Computing Education: Potential and Pitfalls

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