| Shape: 276 rows × 202 columns | |
| Domain: Engineering — this is a survey dataset about requirements engineering practices for ML-enabled systems. | |
| Column groups (the 202 columns are organized into sections): | |
| D1–D15 — Demographic info: education level, country, company size, role, software/ML experience, team size, management frameworks, programming languages, ML algorithms used, etc. | |
| Q1 — ML lifecycle phase importance ratings (Problem Understanding → Monitoring) | |
| Q2 — ML lifecycle phase difficulty ratings | |
| Q3 — ML lifecycle phase effort ratings | |
| Q4 — Open-ended main problems per lifecycle phase | |
| Q5 — Ranking of main problems | |
| Q6–Q7 — Solution optimality and extra effort | |
| Q8 — Who addresses ML requirements (roles) | |
| Q9 — Elicitation techniques used | |
| Q10 — Documentation methods | |
| Q11 — Non-functional requirements (NFRs) considered | |
| Q12 — Most difficult RE activities | |
| Q13–Q16 — Model deployment and monitoring practices | |
| Q17 — AutoML tool usage | |
| Origin — Survey source URL | |
| "The mountains don't care how tired you are." |