An Analysis of the PROMISE and ISBSG Software Engineering Data Repositories
Bibliographic record
Abstract
There exist two ongoing large repositories of software projects in the software engineering community: the repository of the International Software Benchmarking Standards Group (ISBSG) and the Repository referred to as PROMISE (PRedictOr Models In Software Engineering). Researchers interested in using the datasets have to conduct their own analysis of the datasets within these repositories that figure out what contents are suitable for their purposes. Repositories designed without users (researchers and Industrial) needs in mind greatly are more challenging to use. This paper present an analysis of both repositories and provide users with additional information on these datasets by identifying the topics addressed, highlighting their descriptiveness, and their availability, and by indicating whether or not further details are available in order to enhance their reusability in further empirical studies. Recommendations to both PROMISE managers and datasets owners are also suggested to improve the usefulness of the data provided.
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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 | 0.043 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".