Papers

Peer-reviewed
Apr, 2016

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

First page
320
Last page
329
Language
English
Publishing type
Research paper (international conference proceedings)
DOI
10.1109/ICST.2016.18
Publisher
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.

Link information
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 information
  • DOI : 10.1109/ICST.2016.18
  • ISSN : 2381-2834
  • Web of Science ID : WOS:000391252900030

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