Bibliographic record
Abstract
Recent works have concluded that software code is more repetitive and predictable, i.e. more natural, than English texts. On re-examination, we find that much of the apparent "naturalness" of source code is due to the presence of language specific syntax, especially separators, such as semi-colons and brackets. For example, separators account for 44% of all tokens in our Java corpus. When we follow the NLP practices of eliminating punctuation (e.g., separators) and stopwords (e.g., keywords), we find that code is still repetitive and predictable, but to a lesser degree than previously thought. We suggest that SyntaxTokens be filtered to reduce noise in code recommenders. Unlike the code written for a particular project, API code usage is similar across projects: a file is opened and closed in the same manner regardless of domain. When we restrict our n-grams to those contained in the Java API, we find that API usages are highly repetitive. Since API calls are common across programs, researchers have made reliable statistical models to recommend sophisticated API call sequences. Sequential n-gram models were developed for natural languages. Code is usually represented by an AST which contains control and data flow, making n-grams models a poor representation of code. Comparing n-grams to statistical graph representations of the same codebase, we find that graphs are more repetitive and contain higherlevel patterns than n-grams. We suggest that future work focus on statistical code graphs models that accurately capture complex coding patterns. Our replication package makes our scripts and data available to future researchers[1].
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".