Proceedings of the 4th international workshop on Predictor models in software engineering
Bibliographic record
Abstract
It is our great pleasure to welcome you to PROMISE 2008 - the 4th International Workshop on Predictor Models in Software Engineering. This year's workshop continues its tradition of being the premier forum for presentation of research results and experience reports in the area of predictor models applied to software engineering. The theme for this year's workshop is Bridging Research and Industry. Key questions for the workshop are -- How might PROMISE and other researchers better align with the realities of industry? -- How can industry make effective use of research ideas? In keeping with this theme, we have two keynote speakers from industry. Dr. Murray Cantor is an IBM Distinguished Engineer and the governance solutions lead on the IBM Rational Software CTO team. Mr. Chris Beal is a Sun Microsystems Senior Staff Engineer working with Solaris Revenue Product Engineering. We are pleased that PROMISE has become an international event. Our call for papers attracted submissions from Asia, Canada, Europe, and the United States. The program committee accepted over a dozen papers covering a variety of topics, including models related to fault prediction, effort estimation, and requirements engineering. One feature that sets this workshop apart from others is the PROMISE repository of software data sets that are publicly available for research purposes. The repository currently has 57 data sets and has grown at an average rate of 44% annually over the last 3.5 years.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".