DEVELOPMENT OF SCIENTIFIC SOFTWARE: A SYSTEMATIC MAPPING, A BIBLIOMETRICS STUDY, AND A PAPER REPOSITORY
Bibliographic record
Abstract
Scientific and engineering research is heavily dependent on effective development and use of software artifacts. Many of these artifacts are produced by the scientists themselves, rather than by trained software engineers. To address the challenges in this area, a research community often referred to as "Development of Scientific Software" has emerged in the last few decades. As this research area has matured, there has been a sharp increase in the number of papers and results made available, and it has thus become important to summarize and provide an overview about those studies. Through a systematic mapping and bibliometrics study, we have reviewed 130 papers in this area. We present the results of our study in this paper. Also we have made the mapping data available on an online repository which is planned to be updated on a regular basis. The results of our study seem to suggest that many software engineering techniques and activities are being used in the development of scientific software. However, there is still a need for further exploration of the usefulness of specific software engineering techniques (e.g., regarding software maintenance, evolution, refactoring, re(v)-engineering, process and project management) in the scientific context. It is hoped that this article will help (new) researchers get an overview of the research space and help them to understand the trends in the area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.204 | 0.274 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".