Improving Productivity in Software Development: How Humans and AI Need to Join Forces

今天的世界运行并依赖于软件。支持和支持该软件的创建者是我工作背后的动力。我提出的研究旨在了解和指导如何使用人工智能(AI)来提高开发人员的生产率。这一提议来得正是时候，因为人工智能正在几乎所有的科学和知识领域产生颠覆性的影响，软件工程也不例外。人工智能的好处是不难想象的，因为软件开发过程的许多方面都会生成和捕获有用的数据，这是人工智能所依赖的。例如，我们预测软件错误并使用开发人员跟踪数据和用户生成数据自动修复它们的能力，对许多公司来说是游戏规则的改变者。但也有需要注意的风险，包括隐私和数据权。滥用自动化还可能影响公平性(不生成跟踪数据的开发人员任务可能无法识别)，降低对人工智能缺乏信任的开发人员的工作满意度，以及可能对社区多样性产生负面影响的人工智能，因为人工智能可能会给某些人口统计人员带来参与的偏见和障碍。女性在软件开发中的参与率低已经是一个令人担忧的问题。然后，这些问题可能会对软件质量产生下游负面影响。一些人担心人工智能可能会接管开发人员的工作，但另一些人觉得最大的担忧是人类可能高估和信任机器，赋予它们太多自主权。开发人员的工作效率并不容易定义，无论是通过工具还是流程，要提高效率就更难了。开发人员的生产力不应该天真地用编写的代码行、交付的功能数量或其他类似的简单量化措施来衡量。这些措施未能体现发展的创造性和协作性。因此，改进软件工程需要对行为方面有丰富和细致入微的理解，以及跨越无数开发环境的技术可能性。改进工程工具和工程管道很重要，但开发人员技术是如何由它们塑造以及反过来如何塑造行为和社会方面的，这一点不容忽视。开发人员的生产力既受技术因素的影响，也受社会因素的影响，当它们在持续的基础上共同发展时，两者都应该共同优化。例如，人工智能用于自动错误修复的成功和采用是在考虑到人的方面之后才发生的。我的研究计划的目标有三个：1)我将提出并推广适合于研究人类和社会行为与软件工程设计的人工智能技术的共同进化的研究方法；2)我将开发社会技术理论，以捕捉人工智能和数据驱动的自动化如何影响软件工程中的人类和社会行为；以及3)我将设计工具和新过程，以提高个人、团队和开发人员社区的生产率和满意度。

Today's world runs and depends on software. Supporting and enabling the creators of that software is the driving passion behind my work. My proposed research aims to understand and guide how artificial intelligence (AI) can be used to improve developer productivity. This proposal is timely as AI is having a disruptive effect on almost every scientific and knowledge domain, and software engineering is no exception. The benefits of AI are easy to imagine as so many aspects of the software development process generate and capture useful data, which AI relies on. For example, our ability to predict software bugs and automatically repair them using developer trace data and user generated data is a game changer for many companies. But there are risks to be aware of, including privacy and data rights. Misuse of automation may also impact fairness (developer tasks that do not generate trace data may not be recognized), reduced work satisfaction for developers who may lack trust with the AI, and AI that may negatively impact community diversity as AI may introduce biases and barriers to participation for some demographics. Low female participation in software development is already a concern. These issues may then have downstream negative effects on software quality. Some worry that AI may take over developer jobs, but others feel the biggest concern is that humans may overestimate and trust machines and give them too much autonomy. Developer productivity is not easy to define, and it is even harder to improve, whether through tools or processes. Developer productivity should not be naively measured by lines of code authored, number of features delivered or other such simple quantitative measures. These measures fail to capture the creative and collaborative aspects of development. Thus, improving software engineering calls for a rich and nuanced understanding of behavioural aspects, as well as technical possibilities across a myriad of development contexts. Improving engineering tools and the engineering pipeline is important, but how developer technologies are shaped by and in turn how they shape behavioural and social aspects must not be ignored. Developer productivity is impacted by both technical and social factors, and both should be jointly optimized as they co-evolve on a continual basis. For example, the success and adoption of AI for automated bug repair did not happen until human aspects were considered. The goals of my research program are threefold: 1) I will propose and promote research methods that are suitable for studying the co-evolution of human and social behaviours with AI technologies designed for software engineering; 2) I will develop socio-technical theories to capture how AI and data driven automation impacts and may be impacted by human and social behaviours in software engineering; and 3) I will design tools and new processes to improve productivity and satisfaction for individuals, teams and communities of developers.