Sampling configurations from software product lines via probability-aware diversification and SAT solving-Reference-Cited by-同舟云学术

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Sampling configurations from software product lines via probability-aware diversification and SAT solving

Published:2022-09-03 Issue:2 Volume:29 Page:
ISSN:0928-8910
Container-title:Automated Software Engineering
language:en
Short-container-title:Autom Softw Eng

Author:

Xiang Yi,Yang Xiaowei,Huang Han,Huang Zhengxin,Li Miqing

Funder

National Natural Science Foundation of China

Science and Technology Program of Guangzhou

Publisher

Springer Science and Business Media LLC

Subject

Software

Link

https://link.springer.com/content/pdf/10.1007/s10515-022-00348-8.pdf

Reference80 articles.

1. Achlioptas, D., Hammoudeh, Z.S., Theodoropoulos, P.: Fast sampling of perfectly uniform satisfying assignments. In: Beyersdorff, O., Wintersteiger, C.M. (eds.) Theory and applications of satisfiability testing - SAT 2018, pp. 135–147. Springer, Cham (2018)

2. Al-Hajjaji, M., Krieter, S., Thüm, T., et al: IncLing: Efficient Product-Line Testing Using Incremental Pairwise Sampling. In: Proceedings of the 2016 ACM SIGPLAN International Conference on generative programming: concepts and experiences. Association for Computing Machinery, New York, NY, USA, GPCE 2016, pp 144–155 (2016)

3. Alves Pereira, J., Acher, M., Martin, H., et al: Sampling effect on performance prediction of configurable systems: A case study. In: Proceedings of the ACM/SPEC International Conference on performance engineering. association for computing machinery, New York, NY, USA, ICPE’20, p 277-288, https://doi.org/10.1145/3358960.3379137, (2020)

4. Arcuri, A., Briand, L.: A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: Proceedings of the 33rd International Conference on Software Engineering. Association for computing machinery, New York, NY, USA, ICSE’11, pp 1–10 (2011)

5. Arcuri, A., Briand, L.: A hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering. Software Test Verifi Reliab 24(3), 219–250 (2014)

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