- author = "Weiwei Wang and Ruilian Zhao and Ying Shang and Yong Liu2",
- 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 model",
- 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",
