Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Developing minimum viable products (MVPs) is critical for start-up companies to hit the market fast with an accepted level of performance. The US Food and Drug Administration mandates additional nonfunctional requirements in healthcare systems, meaning that the MVP should provide the best availability, privacy, and security. This critical demand is motivating companies to further rely on analytics to optimize the development process. In a collaborative project with Brightsquid, the authors provided a decision-support system based on analogical reasoning to assist in effort estimation, scoping, and assignment of change requests. This experience report proposes a new metric, change request labels, for better prediction. Using different methods for textual-similarity analysis, the authors found that the combination of machine-learning techniques with experts’ manually added labels has the highest prediction accuracy. Better prediction of change impacts allows a company to optimize its resources and provide proper timing of releases to target MVPs. This article is part of a special issue on Actionable Analytics for Software Engineering.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it