Page 141 - 《软件学报》2025年第5期
P. 141

孙百才 等: 代理辅助多任务进化优化引导的             MPI 程序路径覆盖测试用例生成                              2041


                     on Power Systems. Kharagpur: IEEE, 2009. 1–6. [doi: 10.1109/ICPWS.2009.5442650]
                  [2]  Alvioli  M,  Baum  RL.  Parallelization  of  the  TRIGRS  model  for  rainfall-induced  landslides  using  the  message  passing  interface.
                     Environmental Modelling & Software, 2016, 81: 122–135. [doi: 10.1016/j.envsoft.2016.04.002]
                  [3]  Yan J, Zhang J. An efficient method to generate feasible paths for basis path testing. Information Processing Letters, 2008, 107(3–4):
                     87–92. [doi: 10.1016/j.ipl.2008.01.007]
                  [4]  Sun BC, Gong DW, Tian T, Yao XJ. Integrating an ensemble surrogate model’s estimation into test data generation. IEEE Trans. on
                     Software Engineering, 2022, 48(4): 1336–1350. [doi: 10.1109/TSE.2020.3019406]
                  [5]  Qiao KJ, Yu KJ, Qu BY, Liang J, Song H, Yue CT. An evolutionary multitasking optimization framework for constrained multiobjective
                     optimization problems. IEEE Trans. on Evolutionary Computation, 2022, 26(2): 263–277. [doi: 10.1109/TEVC.2022.3145582]
                  [6]  Qiao  KJ,  Liang  J,  Yu  KJ,  Wang  MH,  Qu  BY,  Yue  CT,  Guo  YN.  A  self-adaptive  evolutionary  multi-task  based  constrained  multi-
                     objective evolutionary algorithm. IEEE Trans. on Emerging Topics in Computational Intelligence, 2023, 7(4): 1098–1112. [doi: 10.1109/
                     TETCI.2023.3236633]
                  [7]  Wang C, Wu K, Liu J. Evolutionary multitasking AUC optimization [Research Frontier]. IEEE Computational Intelligence Magazine,
                     2022, 17(2): 67–82. [doi: 10.1109/MCI.2022.3155325]
                  [8]  Wang  Y,  Lin  JQ,  Liu  J,  Sun  GY,  Pang  T.  Surrogate-assisted  differential  evolution  with  region  division  for  expensive  optimization
                     problems with discontinuous responses. IEEE Trans. on Evolutionary Computation, 2022, 26(4): 780–792. [doi: 10.1109/TEVC.2021.
                     3117990]
                  [9]  Si LC, Zhang XY, Tian Y, Yang SS, Zhang LM, Jin YC. Linear subspace surrogate modeling for large-scale expensive single/multi-
                     objective optimization. IEEE Trans. on Evolutionary Computation, 2023. [doi: 10.1109/TEVC.2023.3319640]
                 [10]  Jin YC, Wang HD, Chugh T, Guo D, Miettinen K. Data-driven evolutionary optimization: An overview and case studies. IEEE Trans. on
                     Evolutionary Computation, 2019, 23(3): 442–458. [doi: 10.1109/TEVC.2018.2869001]
                 [11]  Ma  EZ,  Fu  XF,  Wang  X.  Scalable  path  search  for  automated  test  case  generation.  Electronics,  2022,  11(5):  727.  [doi:  10.3390/
                     electronics11050727]
                 [12]  Khari M, Sinha A, Verdú E, Crespo RG. Performance analysis of six meta-heuristic algorithms over automated test suite generation for
                     path coverage-based optimization. Soft Computing, 2020, 24(12): 9143–9160. [doi: 10.1007/s00500-019-04444-y]
                 [13]  Cai GC, Su QH, Hu ZB. Automated test case generation for path coverage by using grey prediction evolution algorithm with improved
                     scatter search strategy. Engineering Applications of Artificial Intelligence, 2021, 106: 104454. [doi: 10.1016/j.engappai.2021.104454]
                 [14]  Semujju SD, Huang H, Liu FQ, Xiang Y, Hao ZF. Search-based software test data generation for path coverage based on a feedback-
                     directed mechanism. Complex System Modeling and Simulation, 2023, 3(1): 12–31. [doi: 10.23919/CSMS.2022.0027]
                 [15]  Tian T, Gong DW, Kuo FC, Liu H. Genetic algorithm based test data generation for MPI parallel programs with blocking communication.
                     Journal of Systems and Software, 2019, 155: 130–144. [doi: 10.1016/j.jss.2019.04.049]
                 [16]  Gong DW, Pan F, Tian T, Yang S, Meng FL. A feedback-directed method of evolutionary test data generation for parallel programs.
                     Information and Software Technology, 2020, 124: 106318. [doi: 10.1016/j.infsof.2020.106318]
                 [17]  Sun  BC,  Wang  JX,  Gong  DW,  Tian  T.  Scheduling  sequence  selection  for  generating  test  data  to  cover  paths  of  MPI  programs.
                     Information and Software Technology, 2019, 114: 190–203. [doi: 10.1016/j.infsof.2019.07.002]
                 [18]  Du XZ, He HM, Liu JL. Test data generation of deterministic MPI parallel program based on path coverage. In: Proc. of the 2021 Int’l
                     Conf. on Frontiers of Electronics, Information and Computation Technologies. Changsha: ACM, 2021. 91. [doi: 10.1145/3474198.3478213]
                 [19]  Sun BC, Gong DW, Yao XJ. Integrating DSGEO into test case generation for path coverage of MPI programs. Information and Software
                     Technology, 2023, 153: 107068. [doi: 10.1016/j.infsof.2022.107068]
                 [20]  Jin  YC.  Surrogate-assisted  evolutionary  computation:  Recent  advances  and  future  challenges.  Swarm  and  Evolutionary  Computation,
                     2011, 1(2): 61–70. [doi: 10.1016/j.swevo.2011.05.001]
                 [21]  Luo  WJ,  Yi  RK,  Yang  B,  Xu  PL.  Surrogate-assisted  evolutionary  framework  for  data-driven  dynamic  optimization.  IEEE  Trans.  on
                     Emerging Topics in Computational Intelligence, 2019, 3(2): 137–150. [doi: 10.1109/TETCI.2018.2872029]
                 [22]  Li GH, Zhang QF, Lin QZ, Gao WF. A three-level radial basis function method for expensive optimization. IEEE Trans. on Cybernetics,
                     2022, 52(7): 5720–5731. [doi: 10.1109/TCYB.2021.3061420]
                 [23]  Zhan  DW,  Xing  HL.  A  fast  kriging-assisted  evolutionary  algorithm  based  on  incremental  learning.  IEEE  Trans.  on  Evolutionary
                     Computation, 2021, 25(5): 941–955. [doi: 10.1109/TEVC.2021.3067015]
                 [24]  Jiao  RW,  Xue  B,  Zhang  MJ.  Investigating  the  correlation  amongst  the  objective  and  constraints  in  Gaussian  process-assisted  highly
                     constrained  expensive  optimization.  IEEE  Trans.  on  Evolutionary  Computation,  2022,  26(5):  872–885.  [doi:  10.1109/TEVC.2021.
                     3120980]
   136   137   138   139   140   141   142   143   144   145   146