Organizations have a variety of options to help their software developers become their most productive selves, from modifying office layouts, to investing in better tools, to cleaning up the source code. But which options will have the biggest impact? Drawing from the literature in software engineering and industrial/organizational psychology to identify factors that correlate with productivity, we designed a survey that asked 622 developers across 3 companies about these productivity factors and about self-rated productivity. Our results suggest that the factors that most strongly correlate with self-rated productivity were non-technical factors, such as job enthusiasm, peer support for new ideas, and receiving useful feedback about job performance. Compared to other knowledge workers, our results also suggest that software developers' self-rated productivity is more strongly related to task variety and ability to work remotely.

Maintaining awareness of useful tools is a substantial challenge for developers. Physical newsletters are a simple technique to inform developers about tools. In this paper, we evaluate such a technique, called Testing on the Toilet, by performing a mixed-methods case study. We first quantitatively evaluate how effective this technique is by applying statistical causal inference over six years of data about tools used by thousands of developers. We then qualitatively contextualize these results by interviewing and surveying 382 developers, from authors to editors to readers. We found that the technique was generally effective at increasing software development tool use, although the increase varied depending on factors such as the breadth of applicability of the tool, the extent to which the tool has reached saturation, and the memorability of the tool name.

Static analysis tools help developers find bugs, improve code readability, and ensure consistent style across a project. However, these tools can be difficult to smoothly integrate with each other and into the developer workflow, particularly when scaling to large codebases. We present Tricorder, a program analysis platform aimed at building a data-driven ecosystem around program analysis. We present a set of guiding principles for our program analysis tools and a scalable architecture for an analysis platform implementing these principles. We include an empirical, in-situ evaluation of the tool as it is used by developers across Google that shows the usefulness and impact of the platform.