arXiv subjects

Brian Houck

Publications and source records attributed to Brian Houck.

9 recordsLinked to original sources

arXiv2026

EngThrive: Make It Fast and Easy to Do Great Work

Frameworks such as SPACE, DevEx, and DORA established that developer productivity is inherently multidimensional, but left practitioners with a practical question: what should we measure, and how should we use it to improve? This paper introduces Engineering Thrive (EngThrive), a measurement and improvement system developed and deployed across Microsoft's engineering organization. EngThrive organizes productivity around three dimensions - Speed, Ease, and Quality - with Thriving as a guardrail to ensure developer wellbeing improves alongside performance. Within each dimension, outcome-oriented North Star metrics are paired with diagnostic submetrics, combining system telemetry with developer surveys to provide both scale and context. We describe the design principles that guide metric selection, including an approach in which well-chosen metrics align "gaming" behavior with genuine improvement. We also outline the data platform, survey program, and dashboard ecosystem required to operationalize this approach in practice, and present case studies demonstrating how outcome-oriented measurement enables sustained, system-level improvements. Finally, we show that EngThrive functions as a general-purpose evaluation language, applicable not only to developer tools and AI, but to organizational policies, work environments, and other factors that shape how developers experience their work. We offer EngThrive as a concrete model for organizations seeking to move beyond measuring activity toward improving outcomes.

cs.SE↗

arXiv2025

AI Where It Matters: Where, Why, and How Developers Want AI Support in Daily Work

Generative AI is reshaping software work, yet we lack clear guidance on where developers most need support and how to design it responsibly. We report a large-scale, mixed-methods study of N=860 developers examining where, why, and how they seek or limit AI help across SE tasks. Using cognitive appraisal theory, we provide the first empirically validated mapping of developers' task appraisals to AI adoption patterns and Responsible AI (RAI) priorities. Appraisals predict AI openness and use, revealing distinct patterns: strong current use and demand for improvement in core work (e.g., coding, testing); high demand to reduce toil (e.g., documentation, operations); and clear limits for identity- and relationship-centric work (e.g., mentoring). RAI priorities vary by context: reliability and security for systems-facing tasks; transparency, alignment, and steerability to maintain control; and fairness and inclusiveness for human-facing work. Our results offer concrete, contextual guidance for delivering AI where it matters to developers and their work.

cs.SE↗

arXiv2025

The SPACE of AI: Real-World Lessons on AI's Impact on Developers

As artificial intelligence (AI) tools become increasingly embedded in software development workflows, questions persist about their true impact on developer productivity and experience. This paper presents findings from a mixed-methods study examining how developers perceive AI's influence across the dimensions of the SPACE framework: Satisfaction, Performance, Activity, Collaboration and Efficiency. Drawing on survey responses from over 500 developers and qualitative insights from interviews and observational studies, we find that AI is broadly adopted and widely seen as enhancing productivity, particularly for routine tasks. However, the benefits vary, depending on task complexity, individual usage patterns, and team-level adoption. Developers report increased efficiency and satisfaction, with less evidence of impact on collaboration. Organizational support and peer learning play key roles in maximizing AI's value. These findings suggest that AI is augmenting developers rather than replacing them, and that effective integration depends as much on team culture and support structures as on the tools themselves. We conclude with practical recommendations for teams, organizations and researchers seeking to harness AI's potential in software engineering.

cs.HC↗

arXiv2025

Time Warp: The Gap Between Developers' Ideal vs Actual Workweeks in an AI-Driven Era

Software developers balance a variety of different tasks in a workweek, yet the allocation of time often differs from what they consider ideal. Identifying and addressing these deviations is crucial for organizations aiming to enhance the productivity and well-being of the developers. In this paper, we present the findings from a survey of 484 software developers at Microsoft, which aims to identify the key differences between how developers would like to allocate their time during an ideal workweek versus their actual workweek. Our analysis reveals significant deviations between a developer's ideal workweek and their actual workweek, with a clear correlation: as the gap between these two workweeks widens, we observe a decline in both productivity and satisfaction. By examining these deviations in specific activities, we assess their direct impact on the developers' satisfaction and productivity. Additionally, given the growing adoption of AI tools in software engineering, both in the industry and academia, we identify specific tasks and areas that could be strong candidates for automation. In this paper, we make three key contributions: 1) We quantify the impact of workweek deviations on developer productivity and satisfaction 2) We identify individual tasks that disproportionately affect satisfaction and productivity 3) We provide actual data-driven insights to guide future AI automation efforts in software engineering, aligning them with the developers' requirements and ideal workflows for maximizing their productivity and satisfaction.

cs.SE↗

arXiv2024

From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions

Information workers increasingly struggle with productivity challenges in modern workplaces, facing difficulties in managing time and effectively utilizing workplace analytics data for behavioral improvement. Despite the availability of productivity metrics through enterprise tools, workers often fail to translate this data into actionable insights. We present a comprehensive, user-centric approach to address these challenges through AI-based productivity agents tailored to users' needs. Utilizing a two-phase method, we first conducted a survey with 363 participants, exploring various aspects of productivity, communication style, agent approach, personality traits, personalization, and privacy. Drawing on the survey insights, we developed a GPT-4 powered personalized productivity agent that utilizes telemetry data gathered via Viva Insights from information workers to provide tailored assistance. We compared its performance with alternative productivity-assistive tools, such as dashboard and narrative, in a study involving 40 participants. Our findings highlight the importance of user-centric design, adaptability, and the balance between personalization and privacy in AI-assisted productivity tools. By building on these insights, our work provides important guidance for developing more effective productivity solutions, ultimately leading to optimized efficiency and user experiences for information workers.

cs.HC↗

arXiv2022

Advanced Quantitative Techniques to Solve Center of Gravity Problem in Supply Chain

Activities involving transformation of raw materials, various resources and components into final products and also delivering it to the end customer incur a significant cost during the selection of location of a warehouse that can be easily accessed by various actors of the supply chain. To minimize upstream and downstream transportation costs, the center of gravity (CoG) analysis method is used to find the potential warehouse locations for a given demand network which have an impact on the entire supply chain network. Mixed Integer Linear Programming (MILP), an open source tool is developed for implementing CoG method along with certain service level constraints to find optimal potential locations with the least cost. In this paper, an optimization tool has been designed for a forward logistics network with several novel methods like Customer Location Selection (CLS), Customer Packets along with other business heuristics that optimize and enhance the existing MILP to get the optimal solutions with low computational cost and runtime. Finally, recommending an alternative network of facilities which reduces overall costs compared to the existing network. An user interface has also been developed to make a user friendly interaction with the model. We can conclude that this model can significantly help companies reduce costs during the logistics network design.

math.OC↗

arXiv2022

How Developers and Managers Define and Trade Productivity for Quality

In this paper, we present the findings from a survey study to investigate how developers and managers define and trade-off developer productivity and software quality (two related lenses into software development). We found that developers and managers, as cohorts, are not well aligned in their views of what it means to be productive (developers think of productivity in terms of activity, while more managers think of productivity in terms of performance). We also found that developers are not accurate at predicting their managers' views of productivity. In terms of quality, we found that individual developers and managers have quite varied views of what quality means to them, but as cohorts they are closely aligned in their different views, with the majority in both groups defining quality in terms of robustness. Over half of the developers and managers reported that quality can be traded for higher productivity and why this trade-off can be justified, while one third consider quality as a necessary part of productivity that cannot be traded. We also present a new descriptive framework for quality, TRUCE, that we synthesize from the survey responses. We call for more discussion between developers and managers about what they each consider as important software quality attributes, and to have open debate about how software quality relates to developer productivity and what trade-offs should or should not be made.

cs.SE↗

arXiv2021

A Tale of Two Cities: Software Developers Working from Home During the COVID-19 Pandemic

The COVID-19 pandemic has shaken the world to its core and has provoked an overnight exodus of developers that normally worked in an office setting to working from home. The magnitude of this shift and the factors that have accompanied this new unplanned work setting go beyond what the software engineering community has previously understood to be remote work. To find out how developers and their productivity were affected, we distributed two surveys (with a combined total of 3,634 responses that answered all required questions) -- weeks apart to understand the presence and prevalence of the benefits, challenges, and opportunities to improve this special circumstance of remote work. From our thematic qualitative analysis and statistical quantitative analysis, we find that there is a dichotomy of developer experiences influenced by many different factors (that for some are a benefit, while for others a challenge). For example, a benefit for some was being close to family members but for others having family members share their working space and interrupting their focus, was a challenge. Our surveys led to powerful narratives from respondents and revealed the scale at which these experiences exist to provide insights as to how the future of (pandemic) remote work can evolve.

cs.SE↗

arXiv2021

Please Turn Your Cameras On: Remote Onboarding of Software Developers during a Pandemic

The COVID-19 pandemic has impacted the way that software development teams onboard new hires. Previously, most software developers worked in physical offices and new hires onboarded to their teams in the physical office, following a standard onboarding process. However, when companies transitioned employees to work from home due to the pandemic, there was little to no time to develop new onboarding procedures. In this paper, we present a survey of 267 new hires at Microsoft that onboarded to software development teams during the pandemic. We explored their remote onboarding process, including the challenges that the new hires encountered and their social connectedness with their teams. We found that most developers onboarded remotely and never had an opportunity to meet their teammates in person. This leads to one of the biggest challenges faced by these new hires, building a strong social connection with their team. We use these results to provide recommendations for onboarding remote hires.

cs.SE↗