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Documents authored by Zhang, Min

Found 3 Possible Name Variants:

Zhang, Min

Document
DOI: 10.4230/LIPIcs.CONCUR.2015.383
On Reachability Analysis of Pushdown Systems with Transductions: Application to Boolean Programs with Call-by-Reference

Authors: Fu Song, Weikai Miao, Geguang Pu, and Min Zhang

Published in: LIPIcs, Volume 42, 26th International Conference on Concurrency Theory (CONCUR 2015)

Abstract
Pushdown systems with transductions (TrPDSs) are an extension of pushdown systems (PDSs) by associating each transition rule with a transduction, which allows to inspect and modify the stack content at each step of a transition rule. It was shown by Uezato and Minamide that TrPDSs can model PDSs with checkpoint and discrete-timed PDSs. Moreover, TrPDSs can be simulated by PDSs and the predecessor configurations pre^*(C) of a regular set C of configurations can be computed by a saturation procedure when the closure of the transductions in TrPDSs is finite. In this work, we comprehensively investigate the reachability problem of finite TrPDSs. We propose a novel saturation procedure to compute pre^*(C) for finite TrPDSs. Also, we introduce a saturation procedure to compute the successor configurations post^*(C) of a regular set C of configurations for finite TrPDSs. From these two saturation procedures, we present two efficient implementation algorithms to compute pre^*(C) and post^*(C). Finally, we show how the presence of transductions enables the modeling of Boolean programs with call-by-reference parameter passing. The TrPDS model has finite closure of transductions which results in model-checking approach for Boolean programs with call-by-reference parameter passing against safety properties.
Cite as

Fu Song, Weikai Miao, Geguang Pu, and Min Zhang. On Reachability Analysis of Pushdown Systems with Transductions: Application to Boolean Programs with Call-by-Reference. In 26th International Conference on Concurrency Theory (CONCUR 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 42, pp. 383-397, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@InProceedings{song_et_al:LIPIcs.CONCUR.2015.383,
  author =	{Song, Fu and Miao, Weikai and Pu, Geguang and Zhang, Min},
  title =	{{On Reachability Analysis of Pushdown Systems with Transductions: Application to Boolean Programs with Call-by-Reference}},
  booktitle =	{26th International Conference on Concurrency Theory (CONCUR 2015)},
  pages =	{383--397},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-91-0},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{42},
  editor =	{Aceto, Luca and de Frutos Escrig, David},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2015.383},
  URN =		{urn:nbn:de:0030-drops-53624},
  doi =		{10.4230/LIPIcs.CONCUR.2015.383},
  annote =	{Keywords: Verification, Reachability problem, Pushdown system with transductions, Boolean programs with call-by-reference}
}

Zhang, Mingbo

Document
DOI: 10.4230/DagSemProc.06271.9
Decomposition of Differential Polynomials

Authors: Xiao-Shan Gao and Mingbo Zhang

Published in: Dagstuhl Seminar Proceedings, Volume 6271, Challenges in Symbolic Computation Software (2006)

Abstract
We present an algorithm to decompose nonlinear differential polynomials in one variable and with rational functions as coefficients. The algorithm is implemented in Maple for the {em constant field} case. The program can be used to decompose differential polynomials with more than one thousand terms effectively.
Cite as

Xiao-Shan Gao and Mingbo Zhang. Decomposition of Differential Polynomials. In Challenges in Symbolic Computation Software. Dagstuhl Seminar Proceedings, Volume 6271, pp. 1-10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{gao_et_al:DagSemProc.06271.9,
  author =	{Gao, Xiao-Shan and Zhang, Mingbo},
  title =	{{Decomposition of Differential Polynomials}},
  booktitle =	{Challenges in Symbolic Computation Software},
  pages =	{1--10},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{6271},
  editor =	{Wolfram Decker and Mike Dewar and Erich Kaltofen and Stephen Watt},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.06271.9},
  URN =		{urn:nbn:de:0030-drops-7726},
  doi =		{10.4230/DagSemProc.06271.9},
  annote =	{Keywords: Decomposition, differential polynomial, difference polynomial}
}

Zhang, Lingming

Document
DOI: 10.4230/LIPIcs.ECOOP.2018.6
Learning to Accelerate Symbolic Execution via Code Transformation

Authors: Junjie Chen, Wenxiang Hu, Lingming Zhang, Dan Hao, Sarfraz Khurshid, and Lu Zhang

Published in: LIPIcs, Volume 109, 32nd European Conference on Object-Oriented Programming (ECOOP 2018)

Abstract
Symbolic execution is an effective but expensive technique for automated test generation. Over the years, a large number of refined symbolic execution techniques have been proposed to improve its efficiency. However, the symbolic execution efficiency problem remains, and largely limits the application of symbolic execution in practice. Orthogonal to refined symbolic execution, in this paper we propose to accelerate symbolic execution through semantic-preserving code transformation on the target programs. During the initial stage of this direction, we adopt a particular code transformation, compiler optimization, which is initially proposed to accelerate program concrete execution by transforming the source program into another semantic-preserving target program with increased efficiency (e.g., faster or smaller). However, compiler optimizations are mostly designed to accelerate program concrete execution rather than symbolic execution. Recent work also reported that unified settings on compiler optimizations that can accelerate symbolic execution for any program do not exist at all. Therefore, in this work we propose a machine-learning based approach to tuning compiler optimizations to accelerate symbolic execution, whose results may also aid further design of specific code transformations for symbolic execution. In particular, the proposed approach LEO separates source-code functions and libraries through our program-splitter, and predicts individual compiler optimization (i.e., whether a type of code transformation is chosen) separately through analyzing the performance of existing symbolic execution. Finally, LEO applies symbolic execution on the code transformed by compiler optimization (through our local-optimizer). We conduct an empirical study on GNU Coreutils programs using the KLEE symbolic execution engine. The results show that LEO significantly accelerates symbolic execution, outperforming the default KLEE configurations (i.e., turning on/off all compiler optimizations) in various settings, e.g., with the default training/testing time, LEO achieves the highest line coverage in 50/68 programs, and its average improvement rate on all programs is 46.48%/88.92% in terms of line coverage compared with turning on/off all compiler optimizations.
Cite as

Junjie Chen, Wenxiang Hu, Lingming Zhang, Dan Hao, Sarfraz Khurshid, and Lu Zhang. Learning to Accelerate Symbolic Execution via Code Transformation. In 32nd European Conference on Object-Oriented Programming (ECOOP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 109, pp. 6:1-6:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{chen_et_al:LIPIcs.ECOOP.2018.6,
  author =	{Chen, Junjie and Hu, Wenxiang and Zhang, Lingming and Hao, Dan and Khurshid, Sarfraz and Zhang, Lu},
  title =	{{Learning to Accelerate Symbolic Execution via Code Transformation}},
  booktitle =	{32nd European Conference on Object-Oriented Programming (ECOOP 2018)},
  pages =	{6:1--6:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-079-8},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{109},
  editor =	{Millstein, Todd},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2018.6},
  URN =		{urn:nbn:de:0030-drops-92115},
  doi =		{10.4230/LIPIcs.ECOOP.2018.6},
  annote =	{Keywords: Symbolic Execution, Code Transformation, Machine Learning}
}
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