Measuring dependency constraint satisfaction in software release planning using dissimilarity of fuzzy graphs
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Bibliographic record
Abstract
Release planning is a cornerstone problem in incremental software development. It deals with the assignment of requirements to sequence of releases in order to maximize profit, minimize the delay of feedback and return of investment in such a way that dependency and resource constraints are met. Release planning decisions are required at an early stage in the development cycle, when uncertainty is unavoidable in the project estimates. Recently, there are some works concerning the use of fuzzy theory to address issues concerning the uncertainty in the release planning problem: fuzzy effort constraints and fuzzy dependency constraints. In this paper, we study the application of fuzzy theory to handle the uncertainty concerning dependency constraints from a holistic perspective, i.e. the whole set of fuzzy dependency constraints is considered as a fuzzy graph. The satisfaction of dependency constraints in a solution plan is measured by the distance between this plan and an ideal plan (in terms of the dependency constraints). The distance is materialized as the distance between two fuzzy graphs. This is considered to be an essential support for the actual decision-making. All the concepts and the complete approach are illustrated by a case study example.
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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 | 0.001 |
| Open science | 0.000 | 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