Search-based inference of polynomial metamorphic relations

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Jie Zhang2 and Junjie Chen and Dan Hao and 
                 Yingfei Xiong and Bing Xie and Lu Zhang and Hong Mei",
  title =        "Search-based inference of polynomial metamorphic
  booktitle =    "{ACM/IEEE} International Conference on Automated
                 Software Engineering, ASE'14",
  year =         "2014",
  editor =       "Ivica Crnkovic and Marsha Chechik and 
                 Paul Gruenbacher",
  pages =        "701--712",
  address =      "Vasteras, Sweden",
  month =        sep # " 15-19",
  keywords =     "genetic algorithms, genetic programming, PSO, SBSE,
                 Metamorphic testing, Invariant inference, Particle
                 swarm optimization",
  URL =          "",
  DOI =          "doi:10.1145/2642937.2642994",
  timestamp =    "Thu, 15 Jun 2017 21:35:06 +0200",
  biburl =       "",
  bibsource =    "dblp computer science bibliography,",
  size =         "12 pages",
  abstract =     "Metamorphic testing (MT) is an effective methodology
                 for testing those so-called ``non-testable'' programs
                 (e.g., scientific programs), where it is sometimes very
                 difficult for testers to know whether the outputs are
                 correct. In metamorphic testing, metamorphic relations
                 (MRs) (which specify how particular changes to the
                 input of the program under test would change the
                 output) play an essential role. However, testers may
                 typically have to obtain MRs manually.

                 In this paper, we propose a search-based approach to
                 automatic inference of polynomial MRs for a program
                 under test. In particular, we use a set of parameters
                 to represent a particular class of MRs, which we refer
                 to as polynomial MRs, and turn the problem of inferring
                 MRs into a problem of searching for suitable values of
                 the parameters. We then dynamically analyze multiple
                 executions of the program, and use particle swarm
                 optimization to solve the search problem. To improve
                 the quality of inferred MRs, we further use MR
                 filtering to remove some inferred MRs.

                 We also conducted three empirical studies to evaluate
                 our approach using four scientific libraries (including
                 189 scientific functions). From our empirical results,
                 our approach is able to infer many high-quality MRs in
                 acceptable time (i.e., from 9.87 seconds to 1231.16
                 seconds), which are effective in detecting faults with
                 no false detection.",
  notes =        "uses genetic programming to discover metamorphic
                 relations over programs",

Genetic Programming entries for Jie Zhang2 Junjie Chen Dan Hao Yingfei Xiong Bing Xie Lu Zhang Hong Mei