Hybrid Labels are the New Measure!
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.
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 | 0.013 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".