Toward full elasticity in distributed static analysis: The case of callgraph analysis

In this paper we present the design and implementation of a distributed, whole-program static analysis framework that is designed to scale with the size of the input. Our approach is based on the actor programming model and is deployed in the cloud. Our reliance on a cloud cluster provides a degree...

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Publicado:	2017
Materias:	Development environments and tools Distributed and concurrent systems Parallel Performance and scalability Program analysis Program comprehension and visualization Elasticity Scalability Software engineering Concurrent systems Development environment Performance and scalabilities Program comprehension Static analysis
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814503_vPartF130154_n_p442_Garbervetsky http://hdl.handle.net/20.500.12110/paper_97814503_vPartF130154_n_p442_Garbervetsky
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_97814503_vPartF130154_n_p442_Garbervetsky
record_format	dspace
spelling	paper:paper_97814503_vPartF130154_n_p442_Garbervetsky2023-06-08T16:37:30Z Toward full elasticity in distributed static analysis: The case of callgraph analysis Development environments and tools Distributed and concurrent systems Parallel Performance and scalability Program analysis Program comprehension and visualization Elasticity Scalability Software engineering Concurrent systems Development environment Parallel Performance and scalabilities Program analysis Program comprehension Static analysis In this paper we present the design and implementation of a distributed, whole-program static analysis framework that is designed to scale with the size of the input. Our approach is based on the actor programming model and is deployed in the cloud. Our reliance on a cloud cluster provides a degree of elasticity for CPU, memory, and storage resources. To demonstrate the potential of our technique, we show how a typical call graph analysis can be implemented in a distributed setting. The vision that motivates this work is that every large-scale software repository such as GitHub, BitBucket or Visual Studio Online will be able to perform static analysis on a large scale. We experimentally validate our implementation of the distributed call graph analysis using a combination of both synthetic and real benchmarks. To show scalability, we demonstrate how the analysis presented in this paper is able to handle inputs that are almost 10 million lines of code (LOC) in size, without running out of memory. Our results show that the analysis scales well in terms of memory pressure independently of the input size, as we add more virtual machines (VMs). As the number of worker VMs increases, we observe that the analysis time generally improves as well. Lastly, we demonstrate that querying the results can be performed with a median latency of 15 ms. © 2017 Association for Computing Machinery. 2017 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814503_vPartF130154_n_p442_Garbervetsky http://hdl.handle.net/20.500.12110/paper_97814503_vPartF130154_n_p442_Garbervetsky
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Development environments and tools Distributed and concurrent systems Parallel Performance and scalability Program analysis Program comprehension and visualization Elasticity Scalability Software engineering Concurrent systems Development environment Parallel Performance and scalabilities Program analysis Program comprehension Static analysis
spellingShingle	Development environments and tools Distributed and concurrent systems Parallel Performance and scalability Program analysis Program comprehension and visualization Elasticity Scalability Software engineering Concurrent systems Development environment Parallel Performance and scalabilities Program analysis Program comprehension Static analysis Toward full elasticity in distributed static analysis: The case of callgraph analysis
topic_facet	Development environments and tools Distributed and concurrent systems Parallel Performance and scalability Program analysis Program comprehension and visualization Elasticity Scalability Software engineering Concurrent systems Development environment Parallel Performance and scalabilities Program analysis Program comprehension Static analysis
description	In this paper we present the design and implementation of a distributed, whole-program static analysis framework that is designed to scale with the size of the input. Our approach is based on the actor programming model and is deployed in the cloud. Our reliance on a cloud cluster provides a degree of elasticity for CPU, memory, and storage resources. To demonstrate the potential of our technique, we show how a typical call graph analysis can be implemented in a distributed setting. The vision that motivates this work is that every large-scale software repository such as GitHub, BitBucket or Visual Studio Online will be able to perform static analysis on a large scale. We experimentally validate our implementation of the distributed call graph analysis using a combination of both synthetic and real benchmarks. To show scalability, we demonstrate how the analysis presented in this paper is able to handle inputs that are almost 10 million lines of code (LOC) in size, without running out of memory. Our results show that the analysis scales well in terms of memory pressure independently of the input size, as we add more virtual machines (VMs). As the number of worker VMs increases, we observe that the analysis time generally improves as well. Lastly, we demonstrate that querying the results can be performed with a median latency of 15 ms. © 2017 Association for Computing Machinery.
title	Toward full elasticity in distributed static analysis: The case of callgraph analysis
title_short	Toward full elasticity in distributed static analysis: The case of callgraph analysis
title_full	Toward full elasticity in distributed static analysis: The case of callgraph analysis
title_fullStr	Toward full elasticity in distributed static analysis: The case of callgraph analysis
title_full_unstemmed	Toward full elasticity in distributed static analysis: The case of callgraph analysis
title_sort	toward full elasticity in distributed static analysis: the case of callgraph analysis
publishDate	2017
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814503_vPartF130154_n_p442_Garbervetsky http://hdl.handle.net/20.500.12110/paper_97814503_vPartF130154_n_p442_Garbervetsky
_version_	1768544572013543424

Toward full elasticity in distributed static analysis: The case of callgraph analysis

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