Bibliographic record
Abstract
Abstract Rationale research in software development is a challenging area because although there is no shortage of advocates for its value, there is also no shortage of reasons for why rationale is unlikely to be captured in practice. Despite more than 30 years of research there still remains much uncertainty: how useful are the potential benefits and how insurmountable are the barriers? Will the value of the rationale (design and otherwise) justify the cost of collecting it? Although there have been numerous rationale research projects, many, if not most, received little or no empirical evaluation. There also have not been many studies examining what the needs are of the practitioners who would be supported by the rationale. This article discusses the “doom and gloom” predictions of rationale's failure, provides a survey of evaluations of rationale systems, and discusses what we hope is a brighter outlook for rationale research in the future. There are development standards and synergistic research areas that may help with rationale research and its acceptance in the software community with which we should be working. This article also presents the results of a pilot survey of software developers who were asked how they would envision using rationale and what they believe the most important barriers are. Although some results were as expected, there were also some surprises. Research on technology transfer indicates that, among other things, to transition successfully from research into practice we need to understand the need that is being met and demonstrate the value of our approach. Until we have determined how our work is needed by the people we are trying to help we will remain researching under uncertainty.
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 | 0.276 | 0.427 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.070 |
| Scholarly communication | 0.020 | 0.041 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".