skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: How Domain Experts Use an Embedded DSL
Programming tools are increasingly integral to research and analysis in myriad domains, including specialized areas with no formal relation to computer science. Embedded domain-specific languages (eDSLs) have the potential to serve these programmers while placing relatively light implementation burdens on language designers. However, barriers to eDSL use reduce their practical value and adoption. In this paper, we aim to deepen our understanding of how programmers use eDSLs and identify user needs to inform future eDSL designs. We performed a contextual inquiry (9 participants) with domain experts using Mimi, an eDSL for climate change economics modeling. A thematic analysis identified five key themes, including: the interaction between the eDSL and the host language has significant and sometimes unexpected impacts on eDSL user experience, and users preferentially engage with domain-specific communities and code templates rather than host language resources. The needs uncovered in our study offer design considerations for future eDSLs and suggest directions for future DSL usability research.  more » « less
Award ID(s):
2129008
PAR ID:
10549724
Author(s) / Creator(s):
;
Publisher / Repository:
OOPSLA
Date Published:
Journal Name:
Proceedings of the ACM on Programming Languages
Volume:
7
Issue:
OOPSLA2
ISSN:
2475-1421
Page Range / eLocation ID:
1499 to 1530
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, Proceedings of the ACM on Software Engineering)
    Debugging is a critical but challenging task for programmers. This paper proposes ChatDBG, an AI-powered debugging assistant. ChatDBG integrates large language models (LLMs) to significantly enhance the capabilities and user-friendliness of conventional debuggers. ChatDBG lets programmers engage in a collaborative dialogue with the debugger, allowing them to pose complex questions about program state, perform root cause analysis for crashes or assertion failures, and explore open-ended queries like why is x null?. To handle these queries, ChatDBG grants the LLM autonomy to take the wheel: it can act as an independent agent capable of querying and controlling the debugger to navigate through stacks and inspect program state. It then reports its findings and yields back control to the programmer. By leveraging the real-world knowledge embedded in LLMs, ChatDBG can diagnose issues identifiable only through the use of domain-specific reasoning. Our ChatDBG prototype integrates with standard debuggers including LLDB and GDB for native code and Pdb for Python. Our evaluation across a diverse set of code, including C/C++ code with known bugs and a suite of Python code including standalone scripts and Jupyter notebooks, demonstrates that ChatDBG can successfully analyze root causes, explain bugs, and generate accurate fixes for a wide range of real-world errors. For the Python programs, a single query led to an actionable bug fix 67% of the time; one additional follow-up query increased the success rate to 85%. ChatDBG has seen rapid uptake; it has already been downloaded more than 75,000 times. 
    more » « less
  2. ; (, ACM)
    Generative AI is rapidly transforming the practice of programming. At the same time, our understanding of who writes programs, for what purposes, and how they program, has been evolving. By facilitating natural-language-to-code interactions, large language models for code have the potential to open up programming work to a broader range of workers. While existing work finds productivity benefits for expert programmers, interactions with non-experts are less well-studied. In this paper, we consider the future of programming for non-experts through a controlled study of 67 non-programmers. Our study reveals multiple barriers to effective use of large language models of code for non-experts, including several aspects of technical communication. Comparing our results to a prior study of beginning programmers illuminates the ways in which a traditional introductory programming class does and does not equip students to effectively work with generative AI. Drawing on our empirical findings, we lay out a vision for how to empower non-expert programmers to leverage generative AI for a more equitable future of programming. 
    more » « less
  3. ; ; ; ; (, Proceedings of the ACM on Programming Languages)
    Most dynamic languages allow users to turn text into code using various functions, often named eval, with language-dependent semantics. The widespread use of these reflective functions hinders static analysis and prevents compilers from performing optimizations. This paper aims to provide a better sense of why programmers use eval. Understanding why eval is used in practice is key to finding ways to mitigate its negative impact. We have reasons to believe that reflective feature usage is language and application domain-specific; we focus on data science code written in R and compare our results to previous work that analyzed web programming in JavaScript. We analyze 49,296,059 calls to eval from 240,327 scripts extracted from 15,401 R packages. We find that eval is indeed in widespread use; R’s eval is more pervasive and arguably dangerous than what was previously reported for JavaScript. 
    more » « less
  4. (, Dagstuhl reports)
    The report documents the program and outcomes of Dagstuhl Seminar 18061 "Evidence About Programmers for Programming Language Design". The seminar brought together a diverse group of researchers from the fields of computer science education, programming languages, software engineering, human-computer interaction, and data science. At the seminar, participants discussed methods for designing and evaluating programming languages that take the needs of programmers directly into account. The seminar included foundational talks to introduce the breadth of perspectives that were represented among the participants; then, groups formed to develop research agendas for several subtopics, including novice programmers, cognitive load, language features, and love of programming languages. The seminar concluded with a discussion of the current SIGPLAN artifact evaluation mechanism and the need for evidence standards in empirical studies of programming languages. 
    more » « less
  5. ; ; ; ; ; ; ; (, Proceedings on Privacy Enhancing Technologies)
    Differential Privacy (DP) has emerged as a pivotal approach for safeguarding individual privacy in data analysis, yet its practical adoption is often hindered by challenges in the implementation and communication of DP. This paper presents a comprehensive systematization of existing research studies around the usability of DP, synthesizing insights from studies on both the practical use of DP tools and strategies for conveying DP parameters that determine privacy protection levels, such as epsilon. By reviewing and analyzing these studies, we identify core usability challenges, best practices, and critical gaps in current DP tools that affect adoption across diverse user groups, including developers, data analysts, and non-technical stakeholders. Our analysis highlights actionable insights and pathways for future research that emphasizes user-centered design and clear communication, fostering the development of more accessible DP tools that meet practical needs and support broader adoption. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email