A Large Scale Survey of Motivation in Software Development

**Context:** Motivation is known to improve performance. In software development in particular, there has been considerable interest in themotivation 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 relativeeffect on motivation. Since motivation is an internal subjective feeling, we alsoanalyze the validity of the answers. **Method:** We conducted a survey with 66 questions on motivation which wascompleted by 521 developers. Most of the questions used an 11-point scale. Weevaluated the answers’ validity by comparing related questions, comparing toactual behavior on GitHub, and comparison with the same developer in afollow-up survey. **Results:** Validity problems include moderate correlations between answers torelated questions, as well as self-promotion and mistakes in the answers. Despite these problems, predictive analysis—investigating how diverse motivatorsinfluence the probability of high motivation—provided valuable insights. Thecorrelations between the different motivators are low, implying their independence. High values in all 11 motivators predict increased probability of highmotivation. In addition, improvement analysis shows that an increase in mostmotivators predicts an increase in general motivation. **Conclusions:** All 11 motivators indeed support motivation, but only moderately. No single motivator suffices to predict high motivation or motivationimprovement, and each motivator sheds light on a different aspect of motivation. Therefore models based on multiple motivators predict motivationimprovement with up to 94% accuracy, better than any single motivator. **Keywords:** Motivation · Software engineering · Open-source development · Survey validity