From Indentation Shapes to Code Structures
Bibliographic record
Abstract
In a previous study, we showed that indentation was regular across multiple languages and the variance in the level of indentation of a block of revised code is correlated with metrics such as McCabe cyclomatic complexity. Building on that work the current paper investigates the relationship between the "shape'' of the indentation of the revised code block (the "revision'') and the corresponding syntactic structure of the code. We annotated revisions matching these three indentation shapes: "flat'' (all lines are equally indented), "slash'' (indentation becomes increasingly deep), or "bubble'' (indentation increases and then decreases). We then classified the code structure as one of: function definition, loop, expression, comment, etc. We studied thousands of revisions, coming from over 200 software projects, written in a variety of languages. Our study indicates that indentation shape correlates positively with code structure; that is, certain shapes typically correspond to certain code structures. For example, flat shapes commonly correspond to comments while bubble shapes commonly correspond to conditionals and function definitions. These results can form the basis of a tool framework that can analyze code in a language independent way to support browsing targeted to viewing particular code structures such as conditionals or comments.
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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 | 0.003 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".