一句话：提出博士研究计划，系统研究软件工程情感分析工具的使用、应用与最佳实践，并探讨自认技术债和机器人所传递的情感信号。
在软件工程中，人的因素十分重要，开发者在开发过程中会经历多种情绪。已有研究通过 issue、pull request 和 commit message 等制品理解情感的作用，并使用自动化情感分析工具进行检测。本博士论文计划研究这些工具的使用方式、应用场景、最佳实践，以及非自然语言对其性能的影响；同时研究自认技术债和机器人，以理解开发者为何表达情感以及情感传递了什么信号。最终希望为研究者和开发者提供实用建议。
In software engineering the role of human aspects is an important one, especially as developers indicate that they experience a wide range of emotions while developing software. Within software engineering researchers have sought to understand the role emotions and sentiment play in the development of software by studying issues, pull-requests and commit messages. To detect sentiment, automated tools are used, and in this doctoral thesis we plan to study the use of these sentiment analysis tools, their applications, best practices for their usage and the effect of non-natural language on their performance. In addition to studying the application of sentiment analysis tools, we also aim to study self-admitted technical debt and bots in software engineering, to understand why developers express sentiment and what they signal when they express sentiment. Through studying both the application of sentiment analysis tools and the role of sentiment in software engineering, we hope to provide practical recommendations for both researchers and developers.