Proceedings of the Fourth International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We would like to take this opportunity to welcome you to the Fourth International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE 2015) which is co-located with the 37th International Conference on Software Engineering (ICSE 2015) and will be held in Florence on 17th May 2015. We are looking forward to an interdisciplinary workshop in which the intersection of Artificial Intelligence and Software Engineering is explored and extended. We had a total of thirteen submissions. After a rigorous reviewing cycle we accepted seven research papers, one of which was an invited paper. These papers will stimulate many varied discussions and will help to continue the momentum which drives the RAISE workshops. The RAISE workshops provide a platform for discussion of the synergies between AI and software engineering and also help to raise awareness of this work within the wider community. This year we are honoured to have a very exciting keynote talk by John Mylopoulos (from the University of Toronto) entitled Knowledge Representation for Requirements Engineering, and Requirements Engineering for Intelligent Systems. This will set the stage for our workshop and will be a source of great inspiration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it