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Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users. These users are often interested in data-centric tasks, such as spreadsheet manipulation and data wrangling, which are hard to solve if the intent is only communicated using a natural-language description, without including the data. But how do we decide how much data and which data to include in the prompt? This paper makes two contributions towards answering this question. First, we create a dataset of real-world NL-to-code tasks manipulating tabular data, mined from StackOverflow posts. Second, we introduce a cluster-then-select prompting technique, which adds the most representative rows from the input data to the LLM prompt. Our experiments show that LLM performance is indeed sensitive to the amount of data passed in the prompt, and that for tasks with a lot of syntactic variation in the input table, our cluster-then-select technique outperforms a random selection baseline., Comment: Paper accepted to NAACL 2024 (Findings)

Generative AI, such as image generation models and large language models, stands to provide tremendous value to end-user programmers in creative and knowledge workflows. Current research methods struggle to engage end-users in a realistic conversation that balances the actually existing capabilities of generative AI with the open-ended nature of user workflows and the many opportunities for the application of this technology. In this work-in-progress paper, we introduce participatory prompting, a method for eliciting opportunities for generative AI in end-user workflows. The participatory prompting method combines a contextual inquiry and a researcher-mediated interaction with a generative model, which helps study participants interact with a generative model without having to develop prompting strategies of their own. We discuss the ongoing development of a study whose aim will be to identify end-user programming opportunities for generative AI in data analysis workflows., Comment: Proceedings of the 34th Annual Conference of the Psychology of Programming Interest Group (PPIG 2023)

Social and technical trends have significantly changed methods for evaluating and disseminating computing research. Traditional venues for reviewing and publishing, such as conferences and journals, worked effectively in the past. Recently, trends have created new opportunities but also put new pressures on the process of review and dissemination. For example, many conferences have seen large increases in the number of submissions. Likewise, dissemination of research ideas has become dramatically through publication venues such as arXiv.org and social media networks. While these trends predate COVID-19, the pandemic could accelerate longer term changes. Based on interviews with leading academics in computing research, our findings include: (1) Trends impacting computing research are largely positive and have increased the participation, scope, accessibility, and speed of the research process. (2) Challenges remain in securing the integrity of the process, including addressing ways to scale the review process, avoiding attempts to misinform or confuse the dissemination of results, and ensuring fairness and broad participation in the process itself. Based on these findings, we recommend: (1) Regularly polling members of the computing research community, including program and general conference chairs, journal editors, authors, reviewers, etc., to identify specific challenges they face to better understand these issues. (2) An influential body, such as the Computing Research Association regularly issues a "State of the Computing Research Enterprise" report to update the community on trends, both positive and negative, impacting the computing research enterprise. (3) A deeper investigation, specifically to better understand the influence that social media and preprint archives have on computing research, is conducted., Comment: A Computing Community Consortium (CCC) white paper, 12 pages

In 2015, the CCC co-sponsored an industry round table that produced the document "The Future of Computing Research: Industry-Academic Collaborations". Since then, several important trends in computing research have emerged, and this document considers how those trends impact the interaction between academia and industry in computing fields. We reach the following conclusions: - In certain computing disciplines, such as currently artificial intelligence, we observe significant increases in the level of interaction between professors and companies, which take the form of extended joint appointments. - Increasingly, companies are highly motivated to engage both professors and graduate students working in specific technical areas because companies view computing research and technical talent as a core aspect of their business success. - There is also the further potential for principles and values from the academy (e.g., ethics, human-centered approaches, etc.) informing products and R&D roadmaps in new ways through these unique joint arrangements. - This increasing connection between faculty, students, and companies has the potential to change (either positively or negatively) numerous things, including: the academic culture in computing research universities, the research topics that faculty and students pursue, the ability of universities to train undergraduate and graduate students, etc. This report is the first step in engaging the broader computing research community, raising awareness of the opportunities, complexities and challenges of this trend but further work is required. We recommend follow-up to measure the degree and impact of this trend and to establish best practices that are shared widely among computing research institutions., Comment: A Computing Community Consortium (CCC) white paper, 12 pages

Spreadsheets are one of the most widely used programming environments, and are widely deployed in domains like finance where errors can have catastrophic consequences. We present a static analysis specifically designed to find spreadsheet formula errors. Our analysis directly leverages the rectangular character of spreadsheets. It uses an information-theoretic approach to identify formulas that are especially surprising disruptions to nearby rectangular regions. We present ExceLint, an implementation of our static analysis for Microsoft Excel. We demonstrate that ExceLint is fast and effective: across a corpus of 70 spreadsheets, ExceLint takes a median of 5 seconds per spreadsheet, and it significantly outperforms the state of the art analysis., Comment: Appeared at OOPSLA 2018

A recent McKinsey report estimates the economic impact of the Internet of Things (IoT) to be between $3.9 to $11 trillion dollars by 20251 . IoT has the potential to have a profound impact on our daily lives, including technologies for the home, for health, for transportation, and for managing our natural resources. The Internet was largely driven by information and ideas generated by people, but advances in sensing and hardware have enabled computers to more easily observe the physical world. Coupling this additional layer of information with advances in machine learning brings dramatic new capabilities including the ability to capture and process tremendous amounts of data; to predict behaviors, activities, and the future in uncanny ways; and to manipulate the physical world in response. This trend will fundamentally change how people interact with physical objects and the environment. Success in developing value-added capabilities around IoT requires a broad approach that includes expertise in sensing and hardware, machine learning, networked systems, human-computer interaction, security, and privacy. Strategies for making IoT practical and spurring its ultimate adoption also require a multifaceted approach that often transcends technology, such as with concerns over data security, privacy, public policy, and regulatory issues. In this paper we argue that existing best practices in building robust and secure systems are insufficient to address the new challenges that IoT systems will present. We provide recommendations regarding investments in research areas that will help address inadequacies in existing systems, practices, tools, and policies., Comment: A Computing Community Consortium (CCC) white paper, 15 pages