Proceedings of the 2015 Workshop on Challenges in Performance Methods for Software Development
Bibliographic record
Abstract
It is a pleasure to welcome you to the first Workshop on Challenges in Performance Methods for Software Development (WOSP-C'15). This new endeavor seeks to break new ground in the continuing quest to get software performance under control, by looking at where the research in the field should be going, and by discussion of failures as well as successes. The mission of the workshop is to identify promising lines of attack on a persistent and continually-evolving problem: how can developers find and solve performance problems in their designs? The developers' involvement with this problem begins with early design and continues through testing and deployment. WOSP-C gives researchers and practitioners a unique opportunity to share their perspectives. There were ten submissions, of generally high quality, and eight were selected for presentation. As well as the presentations, roughly half of the workshop time will be spent on discussion of issues, as described in the workshop introduction. This workshop is an experiment and an opportunity to recalibrate our thoughts on this challenging field, and perhaps to forge partnerships for future projects. The more controversial the discussion, the more successful it will be.
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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 | 0.093 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".