Kevin M. Storer
Dr. Kevin M. Storer is a Developer Experience Researcher at Google, where he serves as Qualitative Research Lead for the DORA team. Leveraging professional experience in software engineering and postgraduate transdisciplinary training in the social sciences and humanities, Kevin has been leading human-centered studies of software developers since 2015, spanning a diverse set of problem contexts, participant profiles, and research methods.
At Google, Kevin’s research has informed strategic decisions for Google Cloud Platform, Firebase, Go, and Google Assistant. Kevin’s research has been published in top scientific venues on the topics of Artificial Intelligence, Information Retrieval, Embedded Systems, Programming Languages, Ubiquitous Computing, and Interaction Design.
Kevin received his Ph.D. in Informatics from The University of California, Irvine in 2021.
At Google, Kevin’s research has informed strategic decisions for Google Cloud Platform, Firebase, Go, and Google Assistant. Kevin’s research has been published in top scientific venues on the topics of Artificial Intelligence, Information Retrieval, Embedded Systems, Programming Languages, Ubiquitous Computing, and Interaction Design.
Kevin received his Ph.D. in Informatics from The University of California, Irvine in 2021.
Research Areas
Authored Publications
Sort By
Preview abstract
It’s no secret that generative artificial intelligence (GenAI) is rapidly changing the landscape of software development, with discussions about best practices for applying this transformative technology dominating the popular press [cite cite cite]. Perhaps nowhere on Earth have these discussions been more frequent and passionate than inside the organizations dedicated to making GenAI accessible and useful to developers, including at Google. During one such discussion between researchers on our DevOps Research and Assessment (DORA) and Engineering Productivity Research (EPR) teams, we were struck by a recurring finding common to development professionals both inside and outside of Google:
Using GenAI makes developers feel more productive, and developers who trust GenAI use it more.
On the surface, this finding may seem somewhat… obvious. But, for us, it highlighted the deep need to better understand the factors that impact developers’ trust in GenAI systems and ways to foster that trust, so that developers and development firms can yield the most benefit from their investment in GenAI development tools.
Here, we share findings from seven studies conducted at Google, regarding the productivity gains of GenAI use in development, the impacts of developers’ trust on GenAI use, and the factors we’ve observed which positively impact developers’ trust in GenAI. We conclude with five suggested strategies that organizations engaged in software development might employ to foster their developers’ trust in GenAI, thereby increasing their GenAI use and maximizing GenAI-related productivity gains.
View details
"It's Just Everything Outside of the IDE that's the Problem": Information Seeking by Software Developers with Visual Impairments
ACM CHI Conference on Human Factors in Computing Systems (2021)
Preview abstract
Many efforts to increase accessibility in coding for developers with visual impairments (DWVI) have focused on supporting interactions with interactive development environments and source code. However, in order to understand how to appropriately modify and write source code, developers must seek and synthesize information from a variety of disparate and highly technical sources. DWVI might benefit from technological support in this process. But, it is unclear what accessibility issues arise in technical information sources, whether accessibility impacts strategies for seeking technical information, or how best to support DWVI in this process. We conducted observations and interviews with twelve DWVI, about their information seeking behaviors. We found that, DWVI seek information in many of the same sources as their sighted peers, and the accessibility issues identified in technical information sources were similar to those in nontechnical sources. Yet, despite these similarities, accessibility considerations impacted information seeking in highly nuanced ways.
View details
Preview abstract
A growing body of evidence suggests Voice Assistants (VAs) are highly valued by people with vision impairments (PWVI) and much less so by sighted users. Yet, many are deployed in homes where both PWVI and sighted family members reside. Researchers have yet to study whether VA use and perceived benefits are affected in settings where one person has a visual impairment and others do not. We
conducted six in-depth interviews with partners to understand patterns of domestic VA use in mixed-visual-ability families. Although PWVI were more motivated to acquire VAs, used them more frequently, and learned more proactively about their features, partners with vision identified similar benefits and disadvantages of having VAs in their home. We found that the universal usability of VAs both equalizes experience across abilities and presents complex tradeoffs for families—regarding interpersonal relationships, domestic labor, and physical safety—which are weighed against accessibility benefits for PWVI and complicate the decision to fully integrate VAs in the home.
View details
