Five Days of Empirical Software Engineering: the PASED Experience
Bibliographic record
Abstract
Abstract—Acquiring the skills to plan and conduct different kinds of empirical studies is a mandatory requirement for graduate students working in the field of software engineering. These skills typically can only be developed based on the teaching and experience of the students ’ supervisor, because of the lack of specific, practical courses providing these skills. To fill this gap, we organized the first Canadian Summer School on Practical Analyses of Software Engineering Data (PASED). The aim of PASED is to provide—using a “learning by doing ” model of teaching—a solid foundation to software engineering graduate students on conducting empirical studies. This paper describes our experience in organizing the PASED school, i.e., what challenges we encountered, how we designed the lectures and laboratories, and what could be improved in the future based on the participants ’ feedback.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".