Towards an Open Science-Based Framework for Software Engineering Controlled (Quasi-)Experiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Experimental Software Engineering has straightforwardly evolved in the last decades due to the effort of the community in providing consolidated training, teaching and practice. Particularly, for controlled experiments and quasi-experiments, the software engineering community has discussed on the lack of reproducibility and the missing of experimental artifacts sharing policies, such as, dataset, baselines, metamodels, repositories, and scripts. These are, therefore, important issues that jeopardizes controlled experimentation to evolve as rigorous as in millennial sciences as Medicine and Physics. In this ongoing work, it is presented a proposal of a conceptual framework for software engineering controlled experiments and quasi-experiments based on the main principles and practices of Open Science. It is understood that Open Science is one of the pillars to the evolution of science, consequently, to software engineering. The FAIR data, metadata, repositories, curation and provenance are some of the main practices discussed in this paper. Ongoing activities are described, in terms of how they are being performed and their relationship with prospective ones.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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