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DTSTART;TZID=America/Chicago:20251116T083000
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UID:submissions.supercomputing.org_SC25_sess377_tut159@linklings.com
SUMMARY:Better Software for Reproducible Science
DESCRIPTION:David E. Bernholdt (Oak Ridge National Laboratory (ORNL)) and 
 Anshu Dubey (Argonne National Laboratory (ANL))\n\nProducing scientific so
 ftware is a challenge. The high-performance modeling and simulation commun
 ity, in particular, faces the confluence of disruptive changes in computin
 g architectures and new opportunities (and demands) for greatly improved s
 imulation capabilities, especially through coupling physics and scales. Si
 multaneously, computational science and engineering (CSE), as well as othe
 r areas of science, are experiencing an increasing focus on scientific rep
 roducibility and software quality. Large language models (LLMs) can signif
 icantly increase developer productivity through judicious off-loading of t
 asks. However, models can hallucinate, therefore it is important to have a
  good methodology to get the most benefit out of this approach.<br>In this
  tutorial, attendees will learn about practices, processes, and tools to i
 mprove the productivity of those who develop CSE software, increase the su
 stainability of software artifacts, and enhance trustworthiness in their u
 se. We will focus on aspects of scientific software development that are n
 ot adequately addressed by resources developed for industrial software eng
 ineering, offering a strategy for the responsible use of LLMs to enhance d
 eveloper productivity in the context of scientific software development, i
 ncorporating testing strategies for the generated code, and discussing rep
 roducibility considerations in the development and use of scientific softw
 are.\n\nRecording: Livestreamed, Recorded\n\nRegistration Category: Tutori
 al Reg Pass\n\n
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