Towards an Open Science-Based Framework for Software Engineering Controlled (Quasi-)Experiments
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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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 | 0.286 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".