Modern code review is a process in which an incremental code contribution made by a code author is reviewed by one or more peers before it is committed to the version control system. An important element of modern code review is verifying that code contributions adhere to best practices. While some of these best practices can be automatically verified, verifying others is commonly left to human reviewers. This paper reports on the development, deployment, and evaluation of AutoCommenter, a system backed by a large language model that automatically learns and enforces coding best practices. We implemented AutoCommenter for four programming languages (C++, Java, Python, and Go) and evaluated its performance and adoption in a large industrial setting. Our evaluation shows that an end-to-end system for learning and enforcing coding best practices is feasible and has a positive impact on the developer workflow. Additionally, this paper reports on the challenges associated with deploying such a system to tens of thousands of developers and the corresponding lessons learned.

Digital technologies have positively transformed society, but they have also led to undesirable consequences not anticipated at the time of design or development. We posit that insights into past undesirable consequences can help researchers and practitioners gain awareness and anticipate potential adverse effects. To test this assumption, we introduce BLIP, a system that extracts real-world undesirable consequences of technology from online articles, summarizes and categorizes them, and presents them in an interactive, web-based interface. In two user studies with 15 researchers in various computer science disciplines, we found that BLIP substantially increased the number and diversity of undesirable consequences they could list in comparison to relying on prior knowledge or searching online. Moreover, BLIP helped them identify undesirable consequences relevant to their ongoing projects, made them aware of undesirable consequences they "had never considered," and inspired them to reflect on their own experiences with technology.

Policy makers rely on models to track disease, inform health recommendations, and allocate resources. Scientists use models to develop, evaluate, and compare theories. Journalists report on new findings in science, which individuals use to make decisions that impact their nutrition, finances, and other aspects of their lives. Faulty statistical models can lead to spurious estimations of disease spread, findings that do not generalize or reproduce, and a misinformed public. The challenge in developing accurate statistical models lies not in a lack of access to mathematical tools, of which there are many (e.g., R [63], Python [52], SPSS [58], and SAS [24]), but in accurately applying them in conjunction with domain theory, data collection, and statistical knowledge [26, 38]. There is a mismatch between the interfaces existing statistical tools provide and the needs of analysts, especially those who have domain knowledge but lack deep statistical expertise (e.g., many researchers). Current tools separate reasoning about domain theory, study design, and statistical models, but analysts need to reason about all three together in order to author accurate models [26].