RELREA - An Analytical Approach for Evaluating Release Readiness.
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
As part of incremental and iterative software development, decisions about “Is the software product ready to be released at some given release date?” have to be made at the end of each release, sprint or iteration. While this decision is critically important, so far it is largely done either informally or in a simplistic manner, relying on a small set of isolated metrics. In this paper, we present an analytical approach combining the goal-oriented definition of the most relevant readiness metrics with their individual evaluation and their subsequent analytical integration into an aggregated evaluation measure. The applicability of the proposed approach called RELREA is demonstrated for an ongoing public project hosted on GitHub, a web-based hosting service for software development projects. Initial evidence shows that the method is supportive in evaluating release readiness at any point of the development cycle, making projections on the final release readiness and allows determination of bottleneck factors to achieve readiness. Keywords-release date; release readiness; release criteria; fuzzy set; aggregation; case study
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it