Sarah D'Angelo
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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).
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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.
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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.
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Using Logs Data to Identify When Software Engineers Experience Flow or Focused Work
Ben Holtz
ACM CHI Conference on Human Factors in Computing Systems (2023) (to appear)
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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.
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Creativity is an essential part of software engineering. In this column, we describe our approach and insights into how software developers define creativity in their work. Our findings suggest that creativity in software engineering centers on the concepts of clever reuse over pure novelty.
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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)
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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.
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