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.

通过调查问卷和预测分析，本研究确定了 11 个激励因素，并分析了它们对开发者动机的相对影响以及答案的有效性问题。尽管存在一些有效性问题，但对多样化激励因素对高动机概率的影响进行的预测分析提供了有价值的见解。

软件开发中的动机大规模调查及其有效性分析

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

The success of a Pull Request (PR) depends on the responsiveness of the
maintainers and the contributor during the review process. Being aware of the
expected waiting times can lead to better interactions and managed expectations
for both the maintainers and the contributor. In this paper, we propose a
machine-learning approach to predict the first response latency of the
maintainers following the submission of a PR, and the first response latency of
the contributor after receiving the first response from the maintainers. We
curate a dataset of 20 large and popular open-source projects on GitHub and
extract 21 features to characterize projects, contributors, PRs, and review
processes. Using these features, we then evaluate seven types of classifiers to
identify the best-performing models. We also perform permutation feature
importance and SHAP analyses to understand the importance and impact of
different features on the predicted response latencies. Our best-performing
models achieve an average improvement of 33% in AUC-ROC and 58% in AUC-PR for
maintainers, as well as 42% in AUC-ROC and 95% in AUC-PR for contributors
compared to a no-skilled classifier across the projects. Our findings indicate
that PRs submitted earlier in the week, containing an average or slightly
above-average number of commits, and with concise descriptions are more likely
to receive faster first responses from the maintainers. Similarly, PRs with a
lower first response latency from maintainers, that received the first response
of maintainers earlier in the week, and containing an average or slightly
above-average number of commits tend to receive faster first responses from the
contributors. Additionally, contributors with a higher acceptance rate and a
history of timely responses in the project are likely to both obtain and
provide faster first responses.

我们提出了一种基于机器学习的方法来预测在 Pull Request 提交后维护人员首次响应的延迟以及从维护人员获得首次响应后贡献者的首次响应延迟。我们使用 20 个大型热门开源项目的数据集进行特征提取，并使用七种分类器评估最佳模型。通过我们的研究，我们发现在一周的早期提交的 PR，包含平均或略高于平均的提交数量以及简洁的描述更容易获得维护人员更快的首次响应。同样地，从维护人员那获得首次响应早期在一周内且包含平均或略高于平均的提交数量的 PR 更容易获得贡献者更快的首次响应。此外，在项目中具有较高接受率和及时响应历史的贡献者很可能能够更快地获得和提供首次响应。