Understanding Expert Perception in Software Estimation Effort: a Cognitive Approach Using Software Chunks.
Bibliographic record
Abstract
Understanding Expert Perception in Software Estimation Effort: a Cognitive Approach Using Software Chunks. Paulo Roberto da Silva Brum Universit´e de Sherbrooke, Longueuil, Quebec, Canada Abstract: Expert-based estimation is the most common and preferred method to estimate the effort for software development project because it is fast, less expensive and reasonably accuracy. Expert software developers use his intuition and experience during effort estimation task. Estimation by analogy of features is also used to compare the new feature to similar in past development. This paper extends recent studies and shows how experts are able to perceive similarities between two features and to categorize their complexities using software chunks with semantics information based on their intuition and perception of software features cues. The results of this experiment showed experts judgment and analogy to estimate features effort are almost identical and accurate when compared to actual project. The current research proposes a cognitive model to explain expert judgment and analogy for software development effort estimation.
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How this classification was reachedexpand
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".