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DStream: A Streaming-Based Highly Parallel IFDS Framework.

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Title: DStream: A Streaming-Based Highly Parallel IFDS Framework.
Authors: Wang, Xizao1 wangxiz@smail.nju.edu.cn, Zuo, Zhiqiang1 zqzuo@nju.edu.cn, Bu, Lei1 bulei@nju.edu.cn, Zhao, Jianhua1 zhaojh@nju.edu.cn
Source: ICSE: International Conference on Software Engineering. 2023, p2488-2500. 13p.
Subjects: Streaming technology, Parallel computers, Data flow computing, Computer programming, Scalability
Abstract: The IFDS framework supports interprocedural dataflow analysis with distributive flow functions over finite domains. A large class of interprocedural dataflow analysis problems can be formulated as IFDS problems and thus can be solved with the IFDS framework precisely. Unfortunately, scaling IFDS analysis to large-scale programs is challenging in terms of both massive memory consumption and low analysis efficiency. This paper presents DStream, a scalable system dedicated to precise and highly parallel IFDS analysis for large-scale programs. DStream leverages a streaming-based out-of-core computation model to reduce memory footprint significantly and adopts fine-grained data parallelism to achieve efficiency. We implemented a taint analysis as a DStream instance analysis and compared DStream with three state-of-the-art tools. Our experiments validate that DStream outperforms all other tools with average speedups from 4.37x to 14.46x on a commodity PC with limited available memory. Meanwhile, the experiments confirm that DStream successfully scales to large-scale programs which the state-of-the-art tools (e.g., FlowDroid and/or DiskDroid) fail to analyze. [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: DStream: A Streaming-Based Highly Parallel IFDS Framework.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Xizao%22">Wang, Xizao</searchLink><relatesTo>1</relatesTo><i> wangxiz@smail.nju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zuo%2C+Zhiqiang%22">Zuo, Zhiqiang</searchLink><relatesTo>1</relatesTo><i> zqzuo@nju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Bu%2C+Lei%22">Bu, Lei</searchLink><relatesTo>1</relatesTo><i> bulei@nju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Jianhua%22">Zhao, Jianhua</searchLink><relatesTo>1</relatesTo><i> zhaojh@nju.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 2023, p2488-2500. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Streaming+technology%22">Streaming technology</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+computers%22">Parallel computers</searchLink><br /><searchLink fieldCode="DE" term="%22Data+flow+computing%22">Data flow computing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br /><searchLink fieldCode="DE" term="%22Scalability%22">Scalability</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The IFDS framework supports interprocedural dataflow analysis with distributive flow functions over finite domains. A large class of interprocedural dataflow analysis problems can be formulated as IFDS problems and thus can be solved with the IFDS framework precisely. Unfortunately, scaling IFDS analysis to large-scale programs is challenging in terms of both massive memory consumption and low analysis efficiency. This paper presents DStream, a scalable system dedicated to precise and highly parallel IFDS analysis for large-scale programs. DStream leverages a streaming-based out-of-core computation model to reduce memory footprint significantly and adopts fine-grained data parallelism to achieve efficiency. We implemented a taint analysis as a DStream instance analysis and compared DStream with three state-of-the-art tools. Our experiments validate that DStream outperforms all other tools with average speedups from 4.37x to 14.46x on a commodity PC with limited available memory. Meanwhile, the experiments confirm that DStream successfully scales to large-scale programs which the state-of-the-art tools (e.g., FlowDroid and/or DiskDroid) fail to analyze. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/ICSE48619.2023.00208
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 13
        StartPage: 2488
    Subjects:
      – SubjectFull: Streaming technology
        Type: general
      – SubjectFull: Parallel computers
        Type: general
      – SubjectFull: Data flow computing
        Type: general
      – SubjectFull: Computer programming
        Type: general
      – SubjectFull: Scalability
        Type: general
    Titles:
      – TitleFull: DStream: A Streaming-Based Highly Parallel IFDS Framework.
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            NameFull: Wang, Xizao
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            NameFull: Zuo, Zhiqiang
      – PersonEntity:
          Name:
            NameFull: Bu, Lei
      – PersonEntity:
          Name:
            NameFull: Zhao, Jianhua
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          Dates:
            – D: 01
              M: 05
              Text: 2023
              Type: published
              Y: 2023
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            – TitleFull: ICSE: International Conference on Software Engineering
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