Test Data Generation Efficiency Prediction Model for EFSM based on MGGP

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  author =       "Weiwei Wang and Ruilian Zhao and Ying Shang and 
                 Yong Liu",
  title =        "Test Data Generation Efficiency Prediction Model for
                 EFSM based on MGGP",
  booktitle =    "Proceedings of the 8th International Symposium on
                 Search Based Software Engineering, SSBSE 2016",
  year =         "2016",
  editor =       "Federica Sarro and Kalyanmoy Deb",
  volume =       "9962",
  series =       "LNCS",
  pages =        "176--191",
  address =      "Raleigh, North Carolina, USA",
  month =        "8-10 " # oct,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 Multi-gene genetic programming, Extended finite state
                 machine, Test data generation efficiency predictive
  isbn13 =       "978-3-319-47106-8",
  DOI =          "doi:10.1007/978-3-319-47106-8_12",
  abstract =     "Most software testing researches on Extended Finite
                 State Machine (EFSM) have focused on automatic test
                 sequence and data generation. The analysis of test
                 generation efficiency is still inadequate. In order to
                 investigate the relationship between EFSM test data
                 generation efficiency and its influence factors,
                 according to the feasible transition paths of EFSMs, we
                 build a multi-gene genetic programming (MGGP)
                 predictive model to forecast EFSM test data generation
                 efficiency. Besides, considering standard genetic
                 programming (GP) and neural network are commonly
                 employed in predictive models, we conduct experiments
                 to compare MGGP model with GP model and back
                 propagation (BP) neural network model on their
                 predictive ability. The results show that, MGGP model
                 is able to effectively predict EFSM test data
                 generation efficiency, and compared with GP model and
                 BP model, MGGP model's predictive ability is stronger.
                 Moreover, the correlation among the influence factors
                 will not affect its predictive performance.",
  notes =        "GP?

                 co-located with ICSME-2016",

Genetic Programming entries for Weiwei Wang Ruilian Zhao Ying Shang Yong Liu