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: Principles of resilient coding for plant ecophysiologists
Abstract Plant ecophysiology is founded on a rich body of physical and chemical theory, but it is challenging to connect theory with data in unambiguous, analytically rigorous and reproducible ways. Custom scripts written in computer programming languages (coding) enable plant ecophysiologists to model plant processes and fit models to data reproducibly using advanced statistical techniques. Since many ecophysiologists lack formal programming education, we have yet to adopt a unified set of coding principles and standards that could make coding easier to learn, use and modify. We identify eight principles to help in plant ecophysiologists without much programming experience to write resilient code: (i) standardized nomenclature, (ii) consistency in style, (iii) increased modularity/extensibility for easier editing and understanding, (iv) code scalability for application to large data sets, (v) documented contingencies for code maintenance, (vi) documentation to facilitate user understanding; (vii) extensive tutorials and (viii) unit testing and benchmarking. We illustrate these principles using a new R package, {photosynthesis}, which provides a set of analytical and simulation tools for plant ecophysiology. Our goal with these principles is to advance scientific discovery in plant ecophysiology by making it easier to use code for simulation and data analysis, reproduce results and rapidly incorporate new biological understanding and analytical tools.  more » « less
Award ID(s):
1929167
PAR ID:
10308993
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  
Editor(s):
Hauck, Markus
Date Published:
Journal Name:
AoB PLANTS
Volume:
13
Issue:
5
ISSN:
2041-2851
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; ; ; et al (, ACM SIGCHI Conference on Human Factors in Computing)
    Research suggests that parental engagement through Joint Media Engagement (JME) is an important factor in children's learning for coding and programming. Unfortunately, parents with limited technology background may have difficulty supporting their children's access to programming. English-language learning (ELL) families from marginalized communities face particular challenges in understanding and supporting programming, as code is primarily authored using English text. We present BlockStudio, a programming tool for empowering ELL families to jointly engage in introductory coding, using an environment embodying two design principles, text-free and visually concrete. We share a case study involving three community centers serving immigrant and refugee populations. Our findings show ELL families can jointly engage in programming without text, via co-creation and flexible roles, and can create a range of artifacts, indicating understanding of aspects of programming within this environment. We conclude with implications for coding together in ELL families and design ideas for text-free programming research. 
    more » « less
  2. ; ; ; (, International Society of the Learning Sciences)
    Abstract: Embodied Code is a visual programming language in virtual reality (VR). It introduces novices to fundamental computing concepts and immersive game engines through hands-on creative coding. Unlike traditional creative coding toolkits, this system harnesses the visuospatial and kinesthetic affordances of VR to engage users in embodied computer science learning. Coders are afforded considerable flexibility in placing, rearranging, and manipulating elements of code (nodes and connectors) and its output such that space and movement can be leveraged as organizational and conceptual scaffolds. Further, assembling nodes and connectors is guided by two simple principles – input versus output and events versus data. These design principles were adopted to foster analogical mappings between physical experiences of working with code and output in an immersive virtual space and perception and action in the real world. Further, they were purposed for exploring different levels of coding abstraction in classroom use. 
    more » « less
  3. ; ; ; ; ; ; ; ; (, IEEE Transactions on Software Engineering)
    Program comprehension is an important, but hard to measure cognitive process. This makes it difficult to provide suitable programming languages, tools, or coding conventions to support developers in their everyday work. Here, we explore whether functional magnetic resonance imaging (fMRI) is feasible for soundly measuring program comprehension. To this end, we observed 17 participants inside an fMRI scanner while they were comprehending source code. The results show a clear, distinct activation of five brain regions, which are related to working memory, attention, and language processing, which all fit well to our understanding of program comprehension. Furthermore, we found reduced activity in the default mode network, indicating the cognitive effort necessary for program comprehension. We also observed that familiarity with Java as underlying programming language reduced cognitive effort during program comprehension. To gain confidence in the results and the method, we replicated the study with 11 new participants and largely confirmed our findings. Our results encourage us and, hopefully, others to use fMRI to observe programmers and, in the long run, answer questions, such as: How should we train programmers? Can we train someone to become an excellent programmer? How effective are new languages and tools for program comprehension? 
    more » « less
  4. ; ; ; ; (, ACM)
    The rapid adoption of generative AI in software development has impacted the industry, yet its efects on developers with visual impairments remain largely unexplored. To address this gap, we used an Activity Theory framework to examine how developers with visual impairments interact with AI coding assistants. For this purpose, we conducted a study where developers who are visually impaired completed a series of programming tasks using a generative AI coding assistant. We uncovered that, while participants found the AI assistant benefcial and reported signifcant advantages, they also highlighted accessibility challenges. Specifcally, the AI coding assistant often exacerbated existing accessibility barriers and introduced new challenges. For example, it overwhelmed users with an excessive number of suggestions, leading developers who are visually impaired to express a desire for “AI timeouts.” Additionally, the generative AI coding assistant made it more difcult for developers to switch contexts between the AI-generated content and their own code. Despite these challenges, participants were optimistic about the potential of AI coding assistants to transform the coding experience for developers with visual impairments. Our fndings emphasize the need to apply activity-centered design principles to generative AI assistants, ensuring they better align with user behaviors and address specifc accessibility needs. This approach can enable the assistants to provide more intuitive, inclusive, and efective experiences, while also contributing to the broader goal of enhancing accessibility in software development 
    more » « less
  5. ; ; ; ; ; (, 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW))
    The HPC industry is inexorably moving towards an era of extremely heterogeneous architectures, with more devices configured on any given HPC platform and potentially more kinds of devices, some of them highly specialized. Writing a separate code suitable for each target system for a given HPC application is not practical. The better solution is to use directive-based parallel programming models such as OpenMP. OpenMP provides a number of options for offloading a piece of code to devices like GPUs. To select the best option from such options during compilation, most modern compilers use analytical models to estimate the cost of executing the original code and the different offloading code variants. Building such an analytical model for compilers is a difficult task that necessitates a lot of effort on the part of a compiler engineer. Recently, machine learning techniques have been successfully applied to build cost models for a variety of compiler optimization problems. In this paper, we present COMPOFF, a cost model which uses the multi-layer perceptrons to statically estimates the Cost of OpenMP OFFloading. We used six different transformations on a parallel code of Wilson Dslash Operator to support GPU offloading, and we predicted their cost of execution on different GPUs using COMPOFF during compile time. Our results show that this model can predict offloading costs with a root mean squared error in prediction of less than 0.5 seconds. Our preliminary findings indicate that this work will make it much easier and faster for scientists and compiler developers to port legacy HPC applications that use OpenMP to new heterogeneous computing environments. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email