使用日志数据识别软件工程师何时体验心流或专注工作
Using Logs Data to Identify When Software Engineers Experience Flow or Focused Work
AI总结：
本研究采用混合方法设计基于日志和机器学习的指标，通过分离专注工作来识别软件工程师的心流或专注状态，并验证其有效性。
摘要：
除了自我报告数据外，我们缺乏可靠且非侵入性的方法来识别心流。然而，退一步承认心流发生在专注期间，这使我们有机会通过分离专注工作来在测量心流方面取得进展。在这里，我们采用混合方法设计了一种基于日志的指标，该指标利用机器学习和全面的日志数据集合来识别相关动作的时期（表明专注），并使用日记数据和季度调查数据针对专注或心流中的自我报告时间验证了该指标。我们的结果表明，我们可以确定一家大型科技公司的软件工程师何时体验专注工作（包括心流的实例）。该指标适用于工程工作，但可以在其他领域利用以非破坏性地测量人们何时体验专注。未来的研究可以在此工作基础上识别与心流其他方面相关的信号。
英文摘要：
Beyond self-report data, we lack reliable and non-intrusive methods for identifying flow. However, taking a step back and acknowledging that flow occurs during periods of focus gives us the opportunity to make progress towards measuring flow by isolating focused work. Here, we take a mixed-methods approach to design a logs-based metric that leverages machine learning and a comprehensive collection of logs data to identify periods of related actions (indicating focus), and validate this metric against self-reported time in focus or flow using diary data and quarterly survey data. Our results indicate that we can determine when software engineers at a large technology company experience focused work which includes instances of flow. This metric speaks to engineering work, but can be leveraged in other domains to non-disruptively measure when people experience focus. Future research can build upon this work to identify signals associated with other facets of flow.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
