Using Natural Language Processing for Programming Language Code Classification with Multinomial Naive Bayes
Bibliographic record
Abstract
Classifying Programming Languages scripts is very important task for several reasons such as: automated analysis, code maintenance, code search, quality assurance, and code understanding; this process is similar to processing natural languages, especially high-level languages like Python, Java, C#, C, C++, PHP, JavaScript, and others.Leveraging natural language processing concepts, this research explores the application of the Multinomial Naï ve Bayes (MNB) algorithm to identify and classify programming languages used in source code files.MNB is a relatively simple and fast algorithm for text classification.The study utilizes a dataset comprising 12 programming languages and consists of 12,003 samples, totaling 396,090 lines of code.The MNB algorithm is trained on this diverse dataset, and its performance in classifying programming language source code is evaluated.The results of the study demonstrate an impressive accuracy rate of 95.09% in accurately identifying and classifying programming languages.This high accuracy highlights the effectiveness of the applied NLP techniques, specifically the MNB algorithm, in the classification task.The findings of this research have significant implications for multiprogramming language editors such as Visual Studio Code and Notepad+ or any programming editor.With the automatic recognition of programming languages enabled by this approach, users can conveniently paste source code into these editors, and the system will automatically identify and classify the programming language being used.This functionality enhances the user experience and streamlines the coding process, particularly in multi-language development environments.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".