Bibliographic record
Abstract
Modern programming languages evolve, and need to be updated regularly by adding new features that fill new or unexpected programming needs. Existing approaches to determine new language features are completely manual and are based on language developers' experience, source code analysis, feature requests, on-line discussions and programmer interviews. Although these are acceptable practises, they are subjective, time-consuming, and don't always identify real needs. No research, to our knowledge, has attempted to make the task of language feature identification easier. In this paper, we propose a system-atic approach for identifying the need for new language features with the help of pattern and clone detection tools that work on source code. Our approach semi-automates the task of language feature identification, works quickly, reduces the effort involved, and avoids subjectivity by supporting new feature proposals with evidence. We demonstrate our idea by analyzing a large set of projects written in the TXL language. After detecting and analyzing code patterns, we propose eleven new features that can help improve the TXL language to support ways that it is really used.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".