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DOI: 10.4230/OASIcs.SLATE.2025.11

Bridging Language Barriers: A Comparative Review and Empirical Evaluation of Source-To-Source Transpilers

Authors: André Freitas, Tiago Baptista, and Pedro Rangel Henriques

Published in: OASIcs, Volume 135, 14th Symposium on Languages, Applications and Technologies (SLATE 2025)

Abstract

Source-to-source transpilation plays a pivotal role in modern software engineering by enabling code migration, feature adoption, and cross-language interoperability without sacrificing semantic integrity. The contributions discussed in this paper can be split into two. The first is a comprehensive literature review that aims at defining what transpilers are, traces their historical evolution from early Fortran/COBOL preprocessors to more recent tools like Babel and TypeScript, and examines key parsing methodologies, AST representations, and transformation strategies. The second is an experimental investigation which assesses several popular transpilers - selected by GitHub popularity and unique language-pair capabilities, when applied to an equivalent code snippet designed to sum even numbers and identify the maximum element. The metrics evaluated were the execution time, CPU, memory consumption, output accuracy and usability.

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André Freitas, Tiago Baptista, and Pedro Rangel Henriques. Bridging Language Barriers: A Comparative Review and Empirical Evaluation of Source-To-Source Transpilers. In 14th Symposium on Languages, Applications and Technologies (SLATE 2025). Open Access Series in Informatics (OASIcs), Volume 135, pp. 11:1-11:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{freitas_et_al:OASIcs.SLATE.2025.11,
  author =	{Freitas, Andr\'{e} and Baptista, Tiago and Henriques, Pedro Rangel},
  title =	{{Bridging Language Barriers: A Comparative Review and Empirical Evaluation of Source-To-Source Transpilers}},
  booktitle =	{14th Symposium on Languages, Applications and Technologies (SLATE 2025)},
  pages =	{11:1--11:16},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-387-4},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{135},
  editor =	{Baptista, Jorge and Barateiro, Jos\'{e}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2025.11},
  URN =		{urn:nbn:de:0030-drops-236911},
  doi =		{10.4230/OASIcs.SLATE.2025.11},
  annote =	{Keywords: Source-to-source translation, Code transformation, Parsing, Lexical analysis, Syntax analysis, Semantic analysis, Transpilation}
}

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DOI: 10.4230/OASIcs.SLATE.2021.14

Using Machine Learning for Vulnerability Detection and Classification

Authors: Tiago Baptista, Nuno Oliveira, and Pedro Rangel Henriques

Published in: OASIcs, Volume 94, 10th Symposium on Languages, Applications and Technologies (SLATE 2021)

Abstract

The work described in this paper aims at developing a machine learning based tool for automatic identification of vulnerabilities on programs (source, high level code), that uses an abstract syntax tree representation. It is based on FastScan, using code2seq approach. Fastscan is a recently developed system aimed capable of detecting vulnerabilities in source code using machine learning techniques. Nevertheless, FastScan is not able of identifying the vulnerability type. In the presented work the main goal is to go further and develop a method to identify specific types of vulnerabilities. As will be shown, the goal will be achieved by optimizing the model’s hyperparameters, changing the method of preprocessing the input data and developing an architecture that brings together multiple models to predict different specific vulnerabilities. The preliminary results obtained from the training stage, are very promising. The best f1 metric obtained is 93% resulting in a precision of 90% and accuracy of 85%, according to the performed tests and regarding a trained model to predict vulnerabilities of the injection type.

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Tiago Baptista, Nuno Oliveira, and Pedro Rangel Henriques. Using Machine Learning for Vulnerability Detection and Classification. In 10th Symposium on Languages, Applications and Technologies (SLATE 2021). Open Access Series in Informatics (OASIcs), Volume 94, pp. 14:1-14:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{baptista_et_al:OASIcs.SLATE.2021.14,
  author =	{Baptista, Tiago and Oliveira, Nuno and Henriques, Pedro Rangel},
  title =	{{Using Machine Learning for Vulnerability Detection and Classification}},
  booktitle =	{10th Symposium on Languages, Applications and Technologies (SLATE 2021)},
  pages =	{14:1--14:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-202-0},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{94},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Sim\~{o}es, Alberto and Portela, Filipe and Pereira, Maria Jo\~{a}o},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2021.14},
  URN =		{urn:nbn:de:0030-drops-144315},
  doi =		{10.4230/OASIcs.SLATE.2021.14},
  annote =	{Keywords: Vulnerability Detection, Source Code Analysis, Machine Learning}
}

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Documents authored by Baptista, Tiago

Bridging Language Barriers: A Comparative Review and Empirical Evaluation of Source-To-Source Transpilers

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Using Machine Learning for Vulnerability Detection and Classification

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