Code style analytics for the automatic setting of formatting rules in IDEs: A solution to the Tabs vs. Spaces Debate
Bibliographic record
Abstract
The use of code style is very important since it conveys meaning as well as intent of source code. Developers are used to reading code according to their preferred style but those guidelines of proper style vary among software teams, and even different companies. Code style decisions are typically made by managers of software developers, but we would like to investigate how common the different variations of code style are. There are also automated tools to convert code style in a file, however the tools must be configured manually. In this paper, we present a tool for the collection and analysis of code style metrics. We demonstrate the feasibility of scanning existing source code to automatically generate the code style rules for existing tools. We also look at the results of our data mining to look at trends in source code. We perform a quantitative analysis on source code for questions like: How many functions are in a class, on average? How many lines of code are in a method, on average? We also present graphs of the distribution of these data, as well look at special cases of outliers.
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 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 | 0.002 | 0.003 |
| 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 | 0.002 | 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".