Special issue on Semantics, Applications, and Implementation of Program Generation
Bibliographic record
Abstract
Program generation has the prospect of being an integral part of a wide range of software development processes. Recent studies investigate different aspects of program generation systems, including their semantics, their applications, and their implementation. Existing theories and systems address both high-level (source) language and low-level (machine) language generation. A number of programming languages now support program generation and manipulation, with different goals, implementation techniques, and targeted at different applications. In this context, a PLI workshop dedicated to this theme (SAIG'00) was held in Montreal in September 2000. Following on from this workshop, a special issue of the Journal of Functional Programming will be devoted to the same theme. Full-length, archival-quality submissions are solicited on topics including both theoretical and practical models and tools for building program generation systems, Examples include: [bull ] Semantics, type systems, and implementations for multi-stage languages. [bull ] Run-time specialization systems, e.g. compilers, operating systems. [bull ] High-level program generation (applications, foundations, environments). [bull ] Symbolic computation, linking and explicit substitution, in-lining and macros. Reports on applications of these techniques to real-world problems are especially encouraged, as are submissions that relate ideas and concepts from several of these topics, or bridge the gap between theory and practice. Contributors to SAIG'00 are encouraged to submit, but submission is open to everyone. Papers will be reviewed as regular JFP submissions, and acceptance in the special issue will be based on relevance to the theme. The special issue also welcomes high-quality survey and position papers that would benefit a wide audience. Accepted papers exceeding the space restrictions will be published as regular JFP papers. Submissions should be sent to the guest editor (address below), with a copy to Nasreen Ahmad ( nasreen@dcs.gla.ac.uk ). Submitted articles should be sent in postscript format, preferably gzipped and uuencoded. In addition, please send, as plain text, title, abstract, and contact information. The submission deadline is 1st February 2001. For other submission details, please consult an issue of the Journal of Functional Programming or see the Journal's web page at http://www.dcs.gla.ac.uk/jfp/ .
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.172 | 0.071 |
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".