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Sarah D'Angelo

Sarah D'Angelo

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Google Publications
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    Preview abstract In this installment of Developer Productivity for Humans, we present two lines of research emphasizing the human experience in measuring developer productivity: the experience of flow or focus and the experience of friction during development. View details
    Preview abstract ML enhanced software development tooling is changing the way software engineers develop code. While the development of these tools continues to rise, studies have primarily focused on the accuracy and performance of underlying models, rather than the user experience. Understanding how engineers interact with ML enhanced tooling can help us define what successful interactions with ML based assistance look like. We therefore build upon prior research, by comparing software engineers' perceptions of two types of ML enhanced tools, (1) code completion and (2) code example suggestions. We then use our findings to inform design guidance for ML enhanced software development tooling. This research is intended to spark a growing conversation about the future of ML in software development and guide the design of developer tooling. View details
    Preview abstract 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. View details
    Building and Sustaining Ethnically, Racially, and Gender Diverse Software Engineering Teams: A Study at Google
    Ella Dagan
    Anita Sarma
    Alison Chang
    Jill Dicker
    The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) (2023) (to appear)
    Preview abstract Teams that build software are largely demographically homogeneous. Without diversity, homogeneous perspectives dominate how, why, and for whom software is designed. To understand how teams can successfully build and sustain diversity, we interviewed 11 engineers and 9 managers from some of the most gender and racially diverse teams at Google, a large software company. Qualitatively analyzing the interviews, we found shared approaches to recruiting, hiring, and promoting an inclusive environment, all of which create a positive feedback loop. Our findings produce actionable practices that every member of the team can take to increase diversity by fostering a more inclusive software engineering environment. View details
    Preview abstract Software developers write code nearly everyday, ranging from simple straightforward tasks to challenging and creative tasks. As we have seen across domains, AI/ML based assistants are on the rise in the field of computer science. We refer to them as code generation tools or AI/ML enhanced software developing tooling; and it is changing the way developers write code. As we think about how to design and measure the impact of intelligent writing assistants, the approaches used in software engineering and the considerations unique to writing code can provide a different and complementary perspective for the workshop. In this paper, we propose a focus on two themes: (1) measuring the impact of writing assistants and (2) how code writing assistants are changing the way engineers write code. In our discussion of these topics, we outline approaches used in software engineering, considerations unique to writing code, and how the disciplines of prose writing and code writing can learn from each other. We aim to contribute to the development of a taxonomy of writing assistants that includes possible methods of measurement and considers factors unique to the domain (e.g. prose or code). View details
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