用机器学习提前识别程序员倦怠，分析了最有效的模型与数据集。
Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review
- 系统梳理了用于检测程序员倦怠的机器学习方法。
- 发现多数研究通过情绪维度间接预测倦怠，表现最佳的模型未明确给出具体数值。
- 为后续研究提供可复现的模型与数据集选择建议。
倦怠是一种职业综合征，影响着大多数软件工程师。以往研究表明，机器学习技术在早期检测倦怠方面应用日益广泛。本文对提出机器学习方法并聚焦于检测软件开发人员及IT专业人士倦怠的研究论文进行了系统性文献综述（SLR）。目标是评估所提机器学习技术的准确率与精确度，并为未来研究者提供可复制或拓展的建议。我们发现，多数原始研究集中于检测情绪或利用情绪维度来预测倦怠。此外，我们还开展了一项横断面研究，以比较不同机器学习方法在情绪检测上的表现，以及哪些数据集在捕捉情绪方面更具潜力和表达力。我们认为，通过识别在情绪检测上表现更优的机器学习工具与数据集，可间接提升倦怠识别能力，本研究将为此重要方向的发展提供有价值参考。
原文摘要 · Abstract (English)
Burnout is an occupational syndrome that, like many other professions, affects the majority of software engineers. Past research studies showed important trends, including an increasing use of machine learning techniques to allow for an early detection of burnout. This paper is a systematic literature review (SLR) of the research papers that proposed machine learning (ML) approaches, and focused on detecting burnout in software developers and IT professionals. Our objective is to review the accuracy and precision of the proposed ML techniques, and to formulate recommendations for future researchers interested to replicate or extend those studies. From our SLR we observed that a majority of primary studies focuses on detecting emotions or utilise emotional dimensions to detect or predict the presence of burnout. We also performed a cross-sectional study to detect which ML approach shows a better performance at detecting emotions; and which dataset has more potential and expressivity to capture emotions. We believe that, by identifying which ML tools and datasets show a better performance at detecting emotions, and indirectly at identifying burnout, our paper can be a valuable asset to progress in this important research direction.
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