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."