論文

査読有り
2016年4月

MuVM: Higher Order Mutation Analysis Virtual Machine for C

9th IEEE International Conference on Software Testing, Verification and Validation (ICST)
  • Susumu Tokumoto
  • ,
  • Kazunori Sakamoto
  • ,
  • Hiroaki Yoshida
  • ,
  • Shinichi Honiden

開始ページ
320
終了ページ
329
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/ICST.2016.18
出版者・発行元
IEEE

Mutation analysis is a method for evaluating the effectiveness of a test suite by seeding faults artificially and measuring the fraction of seeded faults detected by the test suite. The major limitation of mutation analysis is its lengthy execution time because it involves generating, compiling and running large numbers of mutated programs, called mutants. Our tool MuVM achieves a significant runtime improvement by performing higher order mutation analysis using four techniques, metamutation, mutation on virtual machine, higher order split-stream execution, and online adaptation technique. In order to obtain the same behavior as mutating the source code directly, metamutation preserves the mutation location information which may potentially be lost during bitcode compilation and optimization. Mutation on a virtual machine reduces the compilation and testing cost by compiling a program once and invoking a process once. Higher order split-stream execution also reduces the testing cost by executing common parts of the mutants together and splitting the execution at a seeded fault. Online adaptation technique reduces the number of generated mutants by omitting infeasible mutants. Our comparative experiments indicate that our tool is significantly superior to an existing tool, an existing technique (mutation schema generation), and no-split-stream execution in higher order mutation.

Web of Science ® 被引用回数 : 10

リンク情報
DOI
https://doi.org/10.1109/ICST.2016.18
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000391252900030&DestApp=WOS_CPL
ID情報
  • DOI : 10.1109/ICST.2016.18
  • ISSN : 2381-2834
  • Web of Science ID : WOS:000391252900030

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