3 Search Results for "Chen, Junjie"

Document
Artifact
DOI: 10.4230/DARTS.5.1.6
Scheduling Self-Suspending Tasks: New and Old Results (Artifact)

Authors: Jian-Jia Chen, Tobias Hahn, Ruben Hoeksma, Nicole Megow, and Georg von der Brüggen

Published in: DARTS, Volume 5, Issue 1, Special Issue of the 31st Euromicro Conference on Real-Time Systems (ECRTS 2019)

Abstract
In computing systems, a job may suspend itself (before it finishes its execution) when it has to wait for certain results from other (usually external) activities. For real-time systems, such self-suspension behavior has been shown to induce performance degradation. Hence, the researchers in the real-time systems community have devoted themselves to the design and analysis of scheduling algorithms that can alleviate the performance penalty due to self-suspension behavior. As self-suspension and delegation of parts of a job to non-bottleneck resources is pretty natural in many applications, researchers in the operations research (OR) community have also explored scheduling algorithms for systems with such suspension behavior, called the master-slave problem in the OR community. This paper first reviews the results for the master-slave problem in the OR literature and explains their impact on several long-standing problems for scheduling self-suspending real-time tasks. For frame-based periodic real-time tasks, in which the periods of all tasks are identical and all jobs related to one frame are released synchronously, we explore different approximation metrics with respect to resource augmentation factors under different scenarios for both uniprocessor and multiprocessor systems, and demonstrate that different approximation metrics can create different levels of difficulty for the approximation. Our experimental results show that such more carefully designed schedules can significantly outperform the state-of-the-art.
Cite as

Jian-Jia Chen, Tobias Hahn, Ruben Hoeksma, Nicole Megow, and Georg von der Brüggen. Scheduling Self-Suspending Tasks: New and Old Results (Artifact). In Special Issue of the 31st Euromicro Conference on Real-Time Systems (ECRTS 2019). Dagstuhl Artifacts Series (DARTS), Volume 5, Issue 1, pp. 6:1-6:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{chen_et_al:DARTS.5.1.6,
  author =	{Chen, Jian-Jia and Hahn, Tobias and Hoeksma, Ruben and Megow, Nicole and von der Br\"{u}ggen, Georg},
  title =	{{Scheduling Self-Suspending Tasks: New and Old Results}},
  pages =	{6:1--6:3},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2019},
  volume =	{5},
  number =	{1},
  editor =	{Chen, Jian-Jia and Hahn, Tobias and Hoeksma, Ruben and Megow, Nicole and von der Br\"{u}ggen, Georg},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DARTS.5.1.6},
  URN =		{urn:nbn:de:0030-drops-107349},
  doi =		{10.4230/DARTS.5.1.6},
  annote =	{Keywords: Self-suspension, master-slave problem, computational complexity, speedup factors}
}
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-dev.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}
}
Document
DOI: 10.4230/DagRep.7.12.50
Testing and Verification of Compilers (Dagstuhl Seminar 17502)

Authors: Junjie Chen, Alastair F. Donaldson, Andreas Zeller, and Hongyu Zhang

Published in: Dagstuhl Reports, Volume 7, Issue 12 (2018)

Abstract
This report documents the Dagstuhl Seminar 17502 "Testing and Verification of Compilers" that took place during December 10 to 13, 2017, which we provide as a resource for researchers who are interested in understanding the state of the art and open problems in this field, and applying them to this and other areas.
Cite as

Junjie Chen, Alastair F. Donaldson, Andreas Zeller, and Hongyu Zhang. Testing and Verification of Compilers (Dagstuhl Seminar 17502). In Dagstuhl Reports, Volume 7, Issue 12, pp. 50-65, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{chen_et_al:DagRep.7.12.50,
  author =	{Chen, Junjie and Donaldson, Alastair F. and Zeller, Andreas and Zhang, Hongyu},
  title =	{{Testing and Verification of Compilers (Dagstuhl Seminar 17502)}},
  pages =	{50--65},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{7},
  number =	{12},
  editor =	{Chen, Junjie and Donaldson, Alastair F. and Zeller, Andreas and Zhang, Hongyu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.7.12.50},
  URN =		{urn:nbn:de:0030-drops-86763},
  doi =		{10.4230/DagRep.7.12.50},
  annote =	{Keywords: code generation, compiler testing, compiler verification, program analysis, program optimization}
}
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