Cloud-based parallel concolic execution

Ting Chen, Youzheng Feng, Xiapu Luo, Xiaodong Lin, Xiaosong Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Path explosion is one of the biggest challenges hindering the wide application of concolic execution. Although several parallel approaches have been proposed to accelerate concolic execution, they neither scale well nor properly handle resource fluctuations and node failures, which often happen in practice. In this paper, we propose a novel approach, named PACCI, which parallelizes concolic execution and adapts to the drastic changes of computing resources by leveraging cloud infrastructures. PACCI tailors concolic execution to the MapReduce programming model and takes into account the features of cloud infrastructures. In particular, we tackle several challenging issues, such as making the exploration of different program paths independently and constructing an extensible path exploration module to support the prioritization of test inputs from a global perspective. Preliminary experimental results show that PACCI is scalable (e.g., gaining about 20× speedup using 24 nodes) and its efficiency declines slightly about 5% and 6.1% under resource fluctuations and node failures, respectively.
Original languageEnglish
Title of host publicationSANER 2017 - 24th IEEE International Conference on Software Analysis, Evolution, and Reengineering
PublisherIEEE
Pages437-441
Number of pages5
ISBN (Electronic)9781509055012
DOIs
Publication statusPublished - 21 Mar 2017
Event24th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2017 - Klagenfurt, Austria
Duration: 21 Feb 201724 Feb 2017

Conference

Conference24th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2017
Country/TerritoryAustria
CityKlagenfurt
Period21/02/1724/02/17

ASJC Scopus subject areas

  • Software

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