Impact Analysis of Missing Values on the Prediction Accuracy of Analogy-based Software Effort Estimation Method AQUA
Why this work is in the frame
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Bibliographic record
Abstract
Effort estimation by analogy (EBA) is often confronted with missing values. Our former analogy- based method AUQA is able to tolerate missing values in the data set, but it is unclear how the percentage of missing values impacts the prediction accuracy and if there is an upper bound for how big this percentage might become in order to guarantee the applicability of AQUA. This paper investigates these questions through an impact analysis. The impact analysis is conducted for seven data sets being of different size and having different initial percentages of missing values. The major results are that (i) we confirm the intuition that the more missing values, the poorer the prediction accuracy of AQUA; (ii) there is a quadratic dependency between the prediction accuracy and the percentage of missing values; and (Hi) the upper limit of missing values for the applicability of AQUA is determined as 40%. These results are obtained in the context of AQUA. Further analysis is necessary for other ways of applying EBA, such as using different similarity measures or analogy adaptation methods from those used in AQUA. For that purpose, the experimental design in this study can be adapted.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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