Automated Large-Scale Multi-Language Dynamic Program Analysis in the Wild (Tool Insights Paper)

Authors Alex Villazón , Haiyang Sun, Andrea Rosà, Eduardo Rosales , Daniele Bonetta, Isabella Defilippis, Sergio Oporto, Walter Binder

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Author Details

Alex Villazón
  • Universidad Privada Boliviana, Bolivia
Haiyang Sun
  • Università della Svizzera italiana, Switzerland
Andrea Rosà
  • Università della Svizzera italiana, Switzerland
Eduardo Rosales
  • Università della Svizzera italiana, Switzerland
Daniele Bonetta
  • Oracle Labs, United States
Isabella Defilippis
  • Universidad Privada Boliviana, Bolivia
Sergio Oporto
  • Universidad Privada Boliviana, Bolivia
Walter Binder
  • Università della Svizzera italiana, Switzerland


This work has been supported by Oracle (ERO project 1332), Swiss National Science Foundation (scientific exchange project IZSEZ0_177215), Hasler Foundation (project 18012), and by a Bridging Grant with Japan (BG 04-122017).

Cite AsGet BibTex

Alex Villazón, Haiyang Sun, Andrea Rosà, Eduardo Rosales, Daniele Bonetta, Isabella Defilippis, Sergio Oporto, and Walter Binder. Automated Large-Scale Multi-Language Dynamic Program Analysis in the Wild (Tool Insights Paper). In 33rd European Conference on Object-Oriented Programming (ECOOP 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 134, pp. 20:1-20:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


Today’s availability of open-source software is overwhelming, and the number of free, ready-to-use software components in package repositories such as NPM, Maven, or SBT is growing exponentially. In this paper we address two straightforward yet important research questions: would it be possible to develop a tool to automate dynamic program analysis on public open-source software at a large scale? Moreover, and perhaps more importantly, would such a tool be useful? We answer the first question by introducing NAB, a tool to execute large-scale dynamic program analysis of open-source software in the wild. NAB is fully-automatic, language-agnostic, and can scale dynamic program analyses on open-source software up to thousands of projects hosted in code repositories. Using NAB, we analyzed more than 56K Node.js, Java, and Scala projects. Using the data collected by NAB we were able to (1) study the adoption of new language constructs such as JavaScript Promises, (2) collect statistics about bad coding practices in JavaScript, and (3) identify Java and Scala task-parallel workloads suitable for inclusion in a domain-specific benchmark suite. We consider such findings and the collected data an affirmative answer to the second question.

Subject Classification

ACM Subject Classification
  • Software and its engineering → Dynamic analysis
  • Dynamic program analysis
  • code repositories
  • GitHub
  • Node.js
  • Java
  • Scala
  • promises
  • JIT-unfriendly code
  • task granularity


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