Bibliographic record
Abstract
A workshop was held at ICSE 2000 in Limerick, Ireland to further efforts in the development of a standard exchange format (SEF) for data extracted from and about source code. WoSEF (Workshop on Standard Exchange Format) brought together people with expertise in a variety of formats, such as RSF, TA, GraX, FAMIX, XML, and XMI, from across the software engineering discipline. We had five sessions consisting of a presentation and discussion period and a working session with three subgroups. The five sessions were: 1) Survey and Overview, 2) Language-level schemas and APIs, 3) High-level schemas, 4) MOF/XMI/UML and CDIF, and 5) Meta schemas and Typed Graphs. During that time we reviewed previous work and debated a number of important issues. This report includes descriptions of the presentations made during these sessions. The main result of the workshop is the agreement of the majority of participants to work on refining GXL (Graph eXchange Language) to be the SEF. GXL is an XML-based notation that uses attributed, typed graphs as a conceptual data model. It is currently a work in progress with contributors from reverse engineering and graph transformation communities in multiple countries. There is a great deal of work to be done to finalise the syntax and to establish reference models for schemas. Anyone interested is welcome to join the effort and instructions on how to get involved are found at the end of the workshop report. Three papers from the workshop have been reprinted here to promote reflection and encourage participation in the work to develop an SEF.
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.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.473 | 0.355 |
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".