Content-Based Textual File Type Detection at Scale

TL;DR

This paper presents a simple, scalable method for detecting file types based solely on textual content using N-gram features and neural networks, achieving high accuracy across many programming languages.

Contribution

It introduces a language-agnostic N-gram based neural classifier for file type detection that works without relying on file extensions.

Findings

Achieves 85% accuracy across 130+ file types

Effective in classifying files without extensions

Simple model suitable for large-scale analysis

Abstract

Programming language detection is a common need in the analysis of large source code bases. It is supported by a number of existing tools that rely on several features, and most notably file extensions, to determine file types. We consider the problem of accurately detecting the type of files commonly found in software code bases, based solely on textual file content. Doing so is helpful to classify source code that lack file extensions (e.g., code snippets posted on the Web or executable scripts), to avoid misclassifying source code that has been recorded with wrong or uncommon file extensions, and also shed some light on the intrinsic recognizability of source code files. We propose a simple model that (a) use a language-agnostic word tokenizer for textual files, (b) group tokens in 1-/2-grams, (c) build feature vectors based on N-gram frequencies, and (d) use a simple fully connected neural network as classifier. As training set we use textual files extracted from GitHub repositories with at least 1000 stars, using existing file extensions as ground truth. Despite its simplicity the proposed model reaches 85% in our experiments for a relatively high number of recognized classes (more than 130 file types).