Developing Scientific Computing Software: Current Processes and Future Directions
Bibliographic record
Abstract
Considerable emphasis in scientific computing (SC) software development has been placed on the software qualities of performance and correctness. However, other software qualities have received less attention, such as the qualities of usability, maintainability, testability and reusability. Presented in this work is a survey titled "Survey on Developing Scientific Computing Software," which is apparently the first conducted to explore the current approaches to SC software development and to determine which qualities of SC software are in most need of improvement. From the survey, we found that systematic development process is frequently not adopted in the SC software community, since 58% of respondents mentioned that their entire development process potentially consists only of coding and debugging. Moreover, semi-formal and formal specification is rarely used when developing SC software, which is suggested by the fact that 70% of respondents indicate that they only use informal specification. In terms of the problems in SC software development, which are discovered by analyzing the survey results, a solution is proposed to improve the quality of SC software by using SE methodologies, concretely, using a modified Parnas' Rational Design Process (PRDP) and the Unified Software Development Process (USDP). A comparison of the two candidate processes is provided to help SC software practitioners determine which of the two processes fits their particular situation. To clarify the discussion of PRDP and USDP for SC software and to help SC software practitioners better understand how to use PRDP and USDP in SC software, a completely documented one-dimensional numerical integration solver (ONIS) example is presented for both PRDP and USDP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".