# Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions

arXiv: https://arxiv.org/abs/1708.08205 · HTML: https://mlsanity.com/paper/1708.08205/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2017.

## Built on by (most cited first, 3 of 3)

- [Reusability in MLOps: Leveraging Ports and Adapters to Build a Microservices Architecture for the Maritime Domain](https://mlsanity.com/paper/2512.08657/) (2025)
- [SymSeqBench: a unified framework for the generation and analysis of rule-based symbolic sequences and datasets](https://mlsanity.com/paper/2512.24977/) (2025)
- [AutoSG: LLM-Driven Solver Generation Solely from Task Prompts for Expensive Optimization](https://mlsanity.com/paper/2605.25658/) (2026)

## Abstract (arXiv)

Scientific code is not production software. Scientific code participates in the evaluation of a scientific hypothesis. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable and replicable.