Advantages, Disadvantages and Misunderstandings About Document Driven Design for Scientific Software
Bibliographic record
Abstract
This study collects qualitative data on the use of a Software Engineering (SE) inspired development process, Document Driven Design (DDD), for developing Scientific Computing Software (SCS). Five SCS projects were redeveloped using DDD and SE best practices. Interviews with the code owners were conducted to assess the impact of the redevelopment. After redevelopment, the code owners agreed that a systematic development process can be beneficial, and they had a positive or neutral response to the software artifacts produced during redevelopment. The code owners, however, felt that the documentation produced by the DDD process requires too great a time commitment and too much up front effort. The concerns expressed by the study participants may be partly a consequence of a delay in ethics approval, which resulted in imperfect communication with the study participants and misunderstandings with respect to the process for creating, and the purpose of, the DDD artifacts. Although the DDD style of documentation has been successful in other domains, the previous claims may not apply in the SCS environment. This study is a first step toward measuring the impact of DDD on SCS. The results of the study are not definitive, but they certainly suggest that further empirical study is warranted.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".