# A Large Scale Survey of Motivation in Software Development and Analysis of its Validity

arXiv: https://arxiv.org/abs/2404.08303 · HTML: https://mlsanity.com/paper/2404.08303/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 115 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits](https://mlsanity.com/paper/2011.06244/) (2020)
- [What happens when software developers are (un)happy](https://mlsanity.com/paper/1707.00432/) (2017)
- [Follow Your Nose -- Which Code Smells are Worth Chasing?](https://mlsanity.com/paper/2103.01861/) (2021)
- [The Shifting Sands of Motivation: Revisiting What Drives Contributors in Open Source](https://mlsanity.com/paper/2101.10291/) (2021)
- [End to End Software Engineering Research](https://mlsanity.com/paper/2112.11858/) (2021)

## Abstract (arXiv)

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.