4th International Workshop on Conducting Empirical Studies in Industry (CESI 2016)
Bibliographic record
Abstract
Few would deny today the importance of empirical studies in the field of Software Engineering. An increasing number of studies are being conducted involving the software industry, but, while literature abounds on idealistic empirical procedures, relatively little is known about the dynamics and complexity of conducting empirical studies in the software industry. How research results are put into action in industrial settings and how much cross company learning takes place through replication of empirical studies in different contexts? What are the impediments when attempting to follow prescriptive procedures in the organizational setting and how to best handle them? These drivers underly the organization of the fourth in a series of workshops, CESI 2016, held on 17th May, 2016 at ICSE 2016. This report summarizes the workshop details and the proceedings of the day.
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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 | 0.145 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.076 | 0.043 |
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".