Automated Large-Scale Multi-Language Dynamic Program Analysis in the Wild (Artifact)

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

doi:10.4230/DARTS.5.2.11

Artifact Description

DARTS.5.2.11.pdf

Filesize: 343 kB
3 pages

Document Identifiers

DOI: 10.4230/DARTS.5.2.11
URN: urn:nbn:de:0030-drops-107881

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

Acknowledgements

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 (Artifact). In Special Issue of the 33rd European Conference on Object-Oriented Programming (ECOOP 2019). Dagstuhl Artifacts Series (DARTS), Volume 5, Issue 2, pp. 11:1-11:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/DARTS.5.2.11

Artifact

DARTS-5-2-11-artifact-fa2151a3fa429d676e7eb2c77d5aad62.tgz (Filesize: 5.51 GB)

Abstract

This artifact provides a preliminary release of NAB, a distributed infrastructure for executing large-scale dynamic program analyses (DPAs). The artifact consists of ready-to-use Docker containers that allow one to run different DPA tools (Deep-Promise, JITProf, and tgp) on Node.js, Java, and Scala projects hosted on GitHub. The artifact enables the reproduction of the figures and tables of the related paper "Automated Large-scale Multi-language Dynamic Program Analysis in the Wild" with pre-collected data (several GBs) and the execution of DPAs on specific sets of GitHub projects.

Subject Classification

ACM Subject Classification

Software and its engineering → Dynamic analysis

Keywords

Dynamic program analysis
code repositories
GitHub
Node.js
Java
Scala
promises
JIT-unfriendly code
task granularity

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References

Oracle Corporation. Java SE HotSpot at a Glance. https://www.oracle.com/technetwork/java/javase/tech/index-jsp-136373.html, 2018.
L. Gong, M. Pradel, and K. Sen. JITProf: Pinpointing JIT-unfriendly JavaScript Code. In ESEC/FSE, pages 357-368, 2015.
A. Rosà, E. Rosales, and W. Binder. Analyzing and Optimizing Task Granularity on the JVM. In CGO, pages 27-37, 2018.
A. Rosà, E. Rosales, and W. Binder. Analysis and Optimization of Task Granularity on the Java Virtual Machine. ACM Transactions on Programming Languages and Systems (TOPLAS), pages 1-47, 2019.
E. Rosales, A. Rosà, and W. Binder. tgp: a Task-Granularity Profiler for the Java Virtual Machine. In APSEC, pages 570-575, 2017.
A. Villazón, H. Sun, A. Rosà, E. Rosales, D. Bonetta, I. Defilippis, S. Oporto, and W. Binder. Automated Large-scale Multi-language Dynamic Program Analysis in the Wild. In ECOOP, London, UK, July 2019.
T. Würthinger, C. Wimmer, C. Humer, A. Wöß, L. Stadler, C. Seaton, G. Duboscq, D. Simon, and M. Grimmer. Practical Partial Evaluation for High-performance Dynamic Language Runtimes. In PLDI, pages 662-676, 2017.