Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Researchers have developed and evaluated many techniques to deliver accurate estimates of the effort required to complete a new software program. Among these, analogy has emerged as a very promising technique, in particular the fuzzy analogy estimation technique that uses the fuzzy logic concepts in order to deal with both categorical and numerical data. The aim of this paper is twofold: (1) evaluate the impact of 3 filters on the predictive ability of single and ensemble fuzzy analogy techniques and (2) assess whether filters could be a source of diversity for fuzzy analogy ensembles. Moreover, it compares the filter single and ensemble fuzzy analogy techniques with fuzzy analogy ensembles built without using feature selection over 6 datasets. The overall results suggest that (1) more accurate estimates are generated when filters were used with single and ensemble fuzzy analogy techniques, (2) filter single fuzzy analogy techniques outperformed filter fuzzy analogy ensembles, and (3) fuzzy analogy ensembles without feature selection were more accurate than filter single and ensemble techniques. Therefore, though the use of feature selection techniques led single and ensemble fuzzy analogy to generate accurate estimations, they failed to be a source of diversity for fuzzy analogy ensembles. Hence, constructing fuzzy analogy homogenous ensembles that combine single fuzzy analogy techniques with different parameter configurations still generate better accuracy than filter fuzzy analogy ensembles. However, further empirical evaluations of filter/wrappers fuzzy analogy ensembles are required in order to confirm or refute these findings.
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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 | 0.001 | 0.003 |
| 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