Estimating Software Effort Using an ANN Model Based on Use Case Points
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a novel Artificial Neural Network (ANN) to predict software effort from use case diagrams based on the Use Case Point (UCP) model. The inputs of this model are software size, productivity and complexity, while the output is the predicted software effort. A multiple linear regression model with three independent variables (same inputs of the ANN) and one dependent variable (effort) is also introduced. Our data repository contains 240 data points in which, 214 are industrial and 26 are educational projects. Both the regression and ANN models were trained using 168 data points and tested using 72 data points. The ANN model was evaluated using the MMER and PRED criteria against the regression model, as well as the UCP model that estimates effort from use cases. Results show that the ANN model is a competitive model with respect to other regression models and can be used as an alternative to predict software effort based on the UCP method.
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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 | 0.001 |
| 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 | 0.002 |
| 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