An Analogy-Based Approach to Estimation of Software Development Effort Using Categorical Data
Bibliographic record
Abstract
Analogy-based software development effort estimation methods have proved to be a viable alternative to other conventional estimation methods since they mimic the human problem solving approach. However, they are limited by their inability to correctly handle categorical data. Therefore, we have proposed, in an earlier work, a new approach called fuzzy analogy which extends classical analogy by incorporating the fuzzy logic concept in the estimation process. The proposed approach may be applied only when the categorical values are derived from numerical data. This paper extends fuzzy analogy to deal with categorical values that are not derived from numerical data. To this aim, we used the fuzzy k-modes algorithm, a well-known clustering technique for large datasets containing categorical values. Thereafter, we evaluate the accuracy of fuzzy analogy construction-based on fuzzy k-modes using the ISBSG R8 dataset. This evaluation shows that our proposed approach leads to significant improvement in estimation accuracy.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".