PyReprism enables consistent code analysis across 187 programming languages
PyReprism: A Shared Lexical Foundation for Polyglot Code Analysis
Software Engineering
Summary
Code analysis tools usually work differently for each programming language, making it tricky to analyze projects that use many languages. The authors present PyReprism, a tool that breaks code into basic parts (tokens) in a way that is consistent for many languages. This lets the same code analysis methods work across 187 languages without custom adjustments. The tool’s tokenizing accuracy matches specialized parsers, and it speeds up analysis by combining what used to require multiple tools into one.
What this means in practice
- •For software engineering teams: Use PyReprism to apply uniform code quality checks and metrics across projects with many programming languages, reducing tool complexity and runtime.
- •For security analysts: Analyze mixed-language codebases more efficiently for vulnerabilities by using a single lexical analysis framework covering many languages consistently.
Authors
Daniel Ogenrwot, Jorge Gonzalo Delgado Cervantes, Richard Sserunjogi, John Businge
Abstract
Empirical studies of polyglot repositories apply the same analyses to every language of a corpus, such as separating code from comments, computing metrics, normalizing identifiers, and screening for duplicated code. Assembling them from specialized tools introduces differences in language coverage, measurement conventions, and workflows. We present PYREPRISM, an open-source Python library and command-line tool that separates language-specific lexical processing from reusable analyses: each language declares its lexical surface, which yields a lossless stream of typed tokens, and every analysis is implemented once on that stream. Across 187 built-in languages, language-specific declarations have a median length of 16 lines, and no language overrides a shared analysis. Against a reference parser, comment and string classification reach F1 scores of 0.994 and 0.958 on 1,412 files in 12 languages that were never used to develop the tokenizer. In a study of a polyglot repository, one tool replaces three; with matched inputs and analysis operations, it takes 2.9s instead of 8.7s on one core. We also characterize where lexical analysis falls short, in complexity estimation and clone screening. Tool: https://github.com/unlv-evol/PyReprism Video: https://youtu.be/IXc-NP5t4GU