A Large Scale Survey of Motivation in Software Development and Analysis
  of its Validity
Idan Amit Dror G. Feitelson
Context: Motivation is known to improve performance. In software development
in particular, there has been considerable interest in the motivation of
contributors to open source. Objective: We identify 11 motivators from the
literature (enjoying programming, ownership of code, learning, self use, etc.),
and evaluate their relative effect on motivation. Since motivation is an
internal subjective feeling, we also analyze the validity of the answers.
Method: We conducted a survey with 66 questions on motivation which was
completed by 521 developers. Most of the questions used an 11 point scale. We
evaluated the validity of the answers validity by comparing related questions,
comparing to actual behavior on GitHub, and comparison with the same developer
in a follow up survey. Results: Validity problems include moderate correlations
between answers to related questions, as well as self promotion and mistakes in
the answers. Despite these problems, predictive analysis, investigating how
diverse motivators influence the probability of high motivation, provided
valuable insights. The correlations between the different motivators are low,
implying their independence. High values in all 11 motivators predict increased
probability of high motivation. In addition, improvement analysis shows that an
increase in most motivators predicts an increase in general motivation.