Automated selection of a software effort estimation model based on accuracy and uncertainty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Software effort estimation plays an important role in the software development process: inaccurate estimation leads to poorutilization of resources and possibly to software project failure. Many software effort estimation techniques have been tried inan effort to develop models that generate optimal estimation accuracy, one of which is machine learning. It is crucial in machinelearning to use a model that will maximize accuracy and minimize uncertainty for the purposes of software effort estimation.However, the process of selecting the best algorithm for estimation is complex and expert-dependent. This paper proposes anapproach to analyzing datasets, automatically building estimation models with various machine learning techniques, and evaluatingand comparing their results to find the model that produces the most accurate and surest estimates for a specific dataset.The proposed approach to automated model selection combines the Bayesian information criterion, correlation coefficients, andPRED measures.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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