DARTS.5.2.11.pdf
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Document
Automated Large-Scale Multi-Language Dynamic Program Analysis in the Wild (Artifact)
Authors
Alex Villazón 
,
Eduardo Rosales ,
-
Part of:
Issue:
Special Issue of the 33rd European Conference on Object-Oriented Programming (ECOOP 2019)
Volume: DARTS, Volume 5 (ECOOP 2019)
Conference: European Conference on Object-Oriented Programming (ECOOP)
Journal: Dagstuhl Artifacts Series (DARTS) - License:
Creative Commons Attribution 3.0 Germany license
- Publication Date: 2019-07-12
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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 As Get 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
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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.
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L. Gong, M. Pradel, and K. Sen. JITProf: Pinpointing JIT-unfriendly JavaScript Code. In ESEC/FSE, pages 357-368, 2015.
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A. Rosà, E. Rosales, and W. Binder. Analyzing and Optimizing Task Granularity on the JVM. In CGO, pages 27-37, 2018.
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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.
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E. Rosales, A. Rosà, and W. Binder. tgp: a Task-Granularity Profiler for the Java Virtual Machine. In APSEC, pages 570-575, 2017.
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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.
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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.