Impacts des techniques de construction des ensembles flous sur la précision d'un modèle d'estimation des coûts de logiciels par analogie floue
Bibliographic record
Abstract
Fuzzy Analogy est une approche d'estimation des couts de developpement de logiciels qui remedie a la problematique d'utilisation des valeurs linguistiques tout le long d'un processus d'estimation base sur la technique CBR (Case-Based reasoning). En effet, Fuzzy Analogy utilise la logique floue pour representer et traiter convenablement les valeurs linguistiques. Dans un precedent travail, nous avons valide Fuzzy Analogy sur la base de projets COCOMO'81 en utilisant une representation floue empirique des differents attributs affectant le cout; les ensembles flous utilises sont associes a des fonctions trapezoidales. Cet article, propose une validation de Fuzzy Analogy sur la meme base de projets avec une representation floue generee automatiquement a partir de donnees historiques. Les ensembles flous et leurs fonctions d'appartenance sont obtenus par l'algorithme FCM (Fuzzy C-Means) et un Algorithme Genetique a Codage Reel (AGCR). Les fonctions d'appartenance de ces ensembles flous peuvent avoir differentes formes : trapezoidale, triangulaire et gaussienne. Une comparaison de la precision des estimations fournies par Fuzzy Analogy, utilisant une technique empirique pour la generation des ensembles flous, avec celle de Fuzzy Analogy, utilisant la technique FCM- AGCR pour la generation des ensembles flous, est aussi presentee dans cet article.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".