NASA · NASA-877 · record R0479

Using MML code generation to create new high-energy astrophysics science software

National Aeronautics and Space Administration · GSFC: Goddard Space Flight Center

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: Software development assistance Derived by keyword rules; see methodology

Problem the AI is intended to solve

We plan to train an LLM on previous large code bases for high-energy astrophysics science pipeline and analysis software. HEASARC is under a mandate to work on next-generation science software to modernize the existing code base and make it easier for new high-energy astrophysics missions to develop data plans at lower cost. By training LLM(s) on existing code the code assistants should be more effective in reducing the FTEs required to implement new systems.

Expected benefits

Modernizing the software that is maintained by HEASARC and reducing the FTEs for new development

System outputs

A code assistant optimized for this type of software

Technology

AI classification: Generative AISource: Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported