Proceedings of the 16th ACM SIGPLAN International Conference on Software Language Engineering
Bibliographic record
Abstract
Welcome to the 16th ACM SIGPLAN International Conference on Software Language Engineering (SLE) held in October 2023 as part of SPLASH 2023. Software Language Engineering (SLE) is a thriving research discipline targeted at establishing an engineering approach to the development, use, and maintenance of software languages, that is, of languages for the specification, modeling and tooling of software. Key topics of interest for SLE include approaches, methodologies and tools for language design and implementation with a focus on techniques for static and behavioral semantics, generative or interpretative approaches (including transformation languages and code generation) as well as meta-languages and tools (including language workbenches). Techniques enabling the testing, simulation or formal verification for language validation purposes are also of particular interest. SLE also accommodates empirical evaluation and experience reports of language engineering tools, such as user studies evaluating usability, performance benchmarks or industrial applications.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".