The value of design rationale information
Bibliographic record
Abstract
A complete and detailed (full) Design Rationale Documentation (DRD) could support many software development activities, such as an impact analysis or a major redesign. However, this is typically too onerous for systematic industrial use as it is not cost effective to write, maintain, or read. The key idea investigated in this article is that DRD should be developed only to the extent required to support activities particularly difficult to execute or in need of significant improvement in a particular context. The aim of this article is to empirically investigate the customization of the DRD by documenting only the information items that will probably be required for executing an activity. This customization strategy relies on the hypothesis that the value of a specific DRD information item depends on its category (e.g., assumptions, related requirements, etc.) and on the activity it is meant to support. We investigate this hypothesis through two controlled experiments involving a total of 75 master students as experimental subjects. Results show that the value of a DRD information item significantly depends on its category and, within a given category, on the activity it supports. Furthermore, on average among activities, documenting only the information items that have been required at least half of the time (i.e., the information that will probably be required in the future) leads to a customized DRD containing about half the information items of a full documentation. We expect that such a significant reduction in DRD information should mitigate the effects of some inhibitors that currently prevent practitioners from documenting design decision rationale.
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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 | 0.038 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".