Page 266 - 《软件学报》2024年第6期

2842 软件学报 2024 年第 35 卷第 6 期

Engineering and Methodology, 2013, 22(1): 6. [doi: 10.1145/2430536.2430540]
[28] Mondal D, Hemmati H, Durocher S. Exploring test suite diversification and code coverage in multi-objective test case selection. In: Proc.
of the 8th IEEE Int’l Conf. on Software Testing, Verification and Validation (ICST). Graz: IEEE, 2015. 1–10. [doi: 10.1109/ICST.2015.
7102588]
[29] Fischer S, Lopez-Herrejon RE, Ramler R, Egyed A. A preliminary empirical assessment of similarity for combinatorial interaction testing
of software product lines. In: Proc. of the 9th Int’l Workshop on Search-based Software Testing. Austin: ACM, 2016. 15–18. [doi: 10.
1145/2897010.2897011]
[30] Henard C, Papadakis M, Perrouin G, Klein J, Le Traon Y. PLEDGE: A product line editor and test generation tool. In: Proc. of the 17th
Int’l Software Product Line Conf. Co-located Workshops. Tokyo: Association for Computing Machinery. 2013. 126–129. [doi: 10.1145/
2499777.2499778]
[31] Al-Hajjaji M, Schulze M, Ryssel U. Similarity analysis of product-line variants. In: Proc. of the 22nd Int’l Systems and Software Product
Line Conf. (Vol. 1). Gothenburg: Association for Computing Machinery, 2018. 226–235. [doi: 10.1145/3233027.3233044]
[32] Devroey X, Perrouin G, Legay A, Schobbens PY, Heymans P. Search-based similarity-driven behavioural SPL testing. In: Proc. of the
10th Int’l Workshop on Variability Modelling of Software-intensive Systems. Salvador: Association for Computing Machinery, 2016.
89–96. [doi: 10.1145/2866614.2866627]
[33] Xiang Y, Huang H, Li MQ, Li SZ, Yang XW. Looking for novelty in search-based software product line testing. IEEE Trans. on Software
Engineering, 2022, 48(7): 2317–2338. [doi: 10.1109/TSE.2021.3057853]
[34] Luo C, Sun BQ, Qiao B, Chen JJ, Zhang HY, Lin JK, Lin QW, Zhang DM. LS-sampling: An effective local search based sampling
approach for achieving high T-wise coverage. In: Proc. of the 29th ACM Joint Meeting on European Software Engineering Conf. and
Symp. on the Foundations of Software Engineering. Athens: ACM, 2021. 1081–1092. [doi: 10.1145/3468264.3468622]
[35] Henard C, Papadakis M, Harman M, Le Traon Y. Combining multi-objective search and constraint solving for configuring large software
product lines. In: Proc. of the 37th IEEE Int’l Conf. on Software Engineering. Florence: IEEE, 2015. 517–528. [doi: 10.1109/ICSE.2015.
69]
[36] Le Berre D, Parrain A. The SAT4J library, release 2.2: System description. Journal on Satisfiability, Boolean Modeling and Computation,
2010, 7(2–3): 59–64. [doi: 10.3233/SAT190075]
[37] Balint A, Schöning U. Choosing probability distributions for stochastic local search and the role of make versus break. In: Proc. of the
15th Int’l Conf. on Theory and Applications of Satisfiability Testing. Trento: Springer, 2012. 16–29. [doi: 10.1007/978-3-642-31612-8_3]
[38] Lehman J, Stanley KO. Abandoning objectives: Evolution through the search for novelty alone. Evolutionary Computation, 2011, 19(2):
189–223. [doi: 10.1162/EVCO_a_00025]
[39] Sánchez AB, Segura S, Parejo JA, Ruiz-Cortés A. Variability testing in the wild: The Drupal case study. Software & Systems Modeling,
2017, 16(1): 173–194. [doi: 10.1007/s10270-015-0459-z]
[40] Xiang Y, Zhou YR, Zheng ZB, Li MQ. Configuring software product lines by combining many-objective optimization and SAT solvers.
ACM Trans. on Software Engineering and Methodology, 2018, 26(4): 14. [doi: 10.1145/3176644]
[41] Baluja S. Population-based incremental learning: A method for integrating genetic search based function optimization and competitive
learning. Schenley Park Pittsburgh: Carnegie Mellon University, 1994.
[42] Mühlenbein H, Paaß G. From recombination of genes to the estimation of distributions I. Binary parameters, In: Proc. of the 4th Int’l
Conf. on Parallel Problem Solving from Nature. Berlin: Springer, 1996. 178–187. [doi: 10.1007/3-540-61723-X_982]
[43] Baranov E, Legay A, Meel KS. Baital: An adaptive weighted sampling approach for improved T-wise coverage. In: Proc. of the 28th
ACM Joint Meeting on European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. New York: ACM,
2020. 1114–1126. [doi: 10.1145/3368089.3409744]
[44] Bagheri E, Ensan F, Gasevic D. Grammar-based test generation for software product line feature models. In: Proc. of the 2012 Conf. of
the Center for Advanced Studies on Collaborative Research. Toronto: Association for Computing Machinery, 2012. 87–101.
[45] Mann HB, Whitney DR. On a test of whether one of two random variables is stochastically larger than the other. The Annals of
Mathematical Statistics, 1947, 18(1): 50–60. [doi: 10.1214/aoms/1177730491]
[46] Vargha A, Delaney HD. A critique and improvement of the CL common language effect size statistics of mcgraw and wong. Journal of
Educational and Behavioral Statistics, 2000, 25(2): 101–132. [doi: 10.3102/10769986025002101]
[47] Friedman M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American
Statistical Association, 1937, 32(200): 675–701. [doi: 10.1080/01621459.1937.10503522]
[48] Audemard G, Simon L. Predicting learnt clauses quality in modern SAT solver. In: Proc. of the 21st Int ’l Joint Conf. on Artificial
Intelligence. Pasadena: Morgan Kaufmann Publishers Inc., 2009. 399–404.