Evaluating Pred(<i>p</i>) and standardized accuracy criteria in software development effort estimation
Bibliographic record
Abstract
Abstract Software development effort estimation (SDEE) plays a primary role in software project management. But choosing the appropriate SDEE technique remains elusive for many project managers and researchers. Moreover, the choice of a reliable estimation accuracy measure is crucial because SDEE techniques behave differently given different accuracy measures. The most widely used accuracy measures in SDEE are those based on magnitude of relative error (MRE) such as mean/median MRE (MMRE/MedMRE) and prediction at level p (Pred(p)), which counts the number of observations where an SDEE technique gave MREs lower than p. However, MRE has proven to be an unreliable accuracy measure, favoring SDEE techniques that underestimate. Consequently, an unbiased measure called standardized accuracy (SA) has been proposed. This paper deals with the Pred(p) and SA measures. We investigate (1) the consistency of Pred(p) and SA as accuracy measures and SDEE technique selectors, and (2) the relationship between Pred(p) and SA. The results suggest that Pred(p) is less biased towards underestimates and generally selects the same best technique as SA. Moreover, SA and Pred(p) measure different aspects of technique performance, and SA may be used as a predictor of Pred(p) by means of the 3 association rules.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".