Kevin M. Storer
Dr. Kevin M. Storer is a multidisciplinary computing researcher and leading public voice on the socio-technical aspects of AI-assisted software development. As the Qualitative Research Lead for Google's DORA team, his work translates rigorous, ethnographic inquiry into public-facing reports and authoritative analyses that shape the conversation for a global audience of developers, executives, and industry decision-makers. Drawing from deep expertise in engineering, social sciences, humanities, and business-to-business research, Kevin's insights have directly influenced the product strategy for applications and services with billions of users worldwide.
Kevin's research has been published in top international venues across diverse domains of computing, including Artificial Intelligence, Human-Centered Programming, Interaction Design, Accessibility, and Embedded Systems. Kevin earned his Ph.D. in Informatics from the University of California, Irvine.
Kevin's research has been published in top international venues across diverse domains of computing, including Artificial Intelligence, Human-Centered Programming, Interaction Design, Accessibility, and Embedded Systems. Kevin earned his Ph.D. in Informatics from the University of California, Irvine.
Authored Publications
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DORA Impact of Generative AI in Software Development
Derek DeBellis
Daniella Villalba
Nathen Harvey
DORA, Google (2025)
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Generative AI is transforming how software is built, offering unprecedented opportunities and raising new challenges. Based on extensive research and developer interviews, this DORA report provides a nuanced understanding of AI's impact on individuals, teams, and organizations.
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Concerns Beyond the Accuracy of AI Output
DORA, Google (2025)
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Generative AI's potential for hallucinations and inaccuracies are by far the most discussed limitation in AI-assisted software development. But, whether developers have other concerns about using generative AI in their coding practice has not been thoroughly explored. This article describes the results of in-depth interviews with developers about their other concerns about generative AI in coding, beyond the tools accuracy, and discusses related policy implications for organizations developing software.
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Measuring software development can help drive impactful change. However, it’s a complex task, and getting started can be daunting as it involves understanding what you should measure, and determining what you can measure. This article provides a guide to selecting a framework that aligns with organizational measurement strategy.
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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.
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DORA Accelerate State of DevOps 2024 Report
Derek DeBellis
Amanda Lewis
Ben Good
Daniella Villalba
Eric Maxwell
Kim Castillo
Michelle Irvine
Nathen Harvey
DORA, Google (2024)
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DORA has been investigating the capabilities, practices, and measures of high-performing technology-driven teams and organizations for over a decade. This is our tenth DORA report. We have heard from more than 39,000 professionals working at organizations of every size and across many different industries globally.
This report highlights the significant impact of AI on software development, explores platform engineering's promises and challenges, and emphasizes user-centricity and stable priorities for organizational success.
Findings from our research can help inform your team's continuous improvement journey.
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How Gen AI Affects the Value of Development Work
DORA, Google (2024)
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This report outlines a series of 10 in-depth interviews aimed at understanding how professional developers ascribe value to their work and whether the use of generative artificial intelligence in development changes how the value of development work is assessment.
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"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)
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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.
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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.
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