Paper Info
Title
BigSpa: An Efficient Interprocedural Static Analysis Engine in the Cloud
Abstract
Static program analysis is widely used in various application areas to solve many practical problems. Although researchers have made significant achievements in static analysis, it is still too challenging to perform sophisticated interprocedural analysis on large-scale modern software. The underlying reason is that interprocedural analysis for large-scale modern software is highly computation- and memory-intensive, leading to poor scalability. We aim to tackle the scalability problem by proposing a novel big data solution for sophisticated static analysis. Specifically, we propose a data-parallel algorithm and a join-process-filter computation model for the CFL-reachability based interprocedural analysis and develop an efficient distributed static analysis engine in the cloud, called BigSpa. Our experiments validated that BigSpa running on a cluster scales greatly to perform precise interprocedural analyses on millions of lines of code, and runs an order of magnitude or more faster than the existing state-of-the-art analysis tools.
Year
DOI
Venue
2019
10.1109/IPDPS.2019.00086
2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Keywords
Field
DocType
static analysis,mapreduce,data parallel computation
Static program analysis,Computer science,Static analysis,Parallel computing,Software,Big data,Scalability,Source lines of code,Cloud computing,Computation
Conference
ISSN
ISBN
Citations 
1530-2075
978-1-7281-1247-3
1
PageRank 
References 
Authors
0.35
17
7
Name
Order
Citations
PageRank
Zhiqiang Zuo1336.53
Rong Gu211017.77
Xi Jiang310.35
Zhaokang Wang4175.17
Yihua Huang586.61
Linzhang Wang641841.32
Li Xuandong767279.78