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

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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.