Performance norms: An approach to rework reduction in software development
Bibliographic record
Abstract
Rework consumes large portions of software engineering budgets. Human factors, and Cognitive Bias in particular, have been shown in other disciplines to be implicated in the kinds of reasoning errors that lead to rework. Research of these phenomena in software engineering lags similar efforts in other disciplines. This study identifies the Performance Norms, standards by which Cognitive Biases are determined to have occurred, in a single but critically important software engineering task: Estimating. Analysis of data from professional practitioners regarding real-life situations indicates that several Performance Norms for Estimating are often `in play', the least important being that assumed in previous, lab-based experiments. Most of these Norms require skills very different from those in which most technical personnel are trained. We conclude that rework reduction efforts will continue to falter until Performance Norms are recognized as key determinants in software engineering practice.
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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 | 0.048 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".