Effect of Feature Subset Selection on Samplings for Performance Prediction of Configurable Systems
Resumo
Organizations require personalized solutions to effectively address users’ needs, and stay competitive in the market. In this context, configurable systems offer numerous configuration options to meet user-specific functional and non-functional requirements. However, although configurability makes these systems flexible and versatile, a simple change can result in serious problems in different software variants, such as performance bottlenecks and security issues. Thus, automated approaches based on machine learning have been developed to facilitate configuration management. Our work aims to expand upon previous findings in this field by assessing their applicability to other scenarios. By introducing more efficient practices, we can contribute to cost reduction, higher software quality, and quicker time-to-market. This is particularly relevant in a global context where software plays a crucial role.
Palavras-chave:
Software Product Lines, Configurable Systems, Machine Learning, Performance Prediction
Referências
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Acher, M., Martin, H., Lesoil, L., Blouin, A., Jézéquel, J.-M., Khelladi, D. E., Barais, O., and Pereira, J. A. (2022). Feature subset selection for learning huge configuration spaces: the case of linux kernel size. In Proceedings of the 26th ACM International Systems and Software Product Line Conference-Volume A, pages 85–96.
Acher, M., Martin, H., Pereira, J. A., Blouin, A., Jézéquel, J.-M., Khelladi, D. E., Lesoil, L., and Barais, O. (2019). Learning very large configuration spaces: What matters for linux kernel sizes. PhD thesis, Inria Rennes-Bretagne Atlantique.
Alves Pereira, J., Acher, M., Martin, H., and Jézéquel, J.-M. (2020). Sampling effect on performance prediction of configurable systems: A case study. page 277–288, New York, NY, USA. Association for Computing Machinery.
Amand, B., Cordy, M., Heymans, P., Acher, M., Temple, P., and Jézéquel, J.-M. (2019). Towards learning-aided configuration in 3d printing: Feasibility study and application to defect prediction. In Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems, page 7. ACM.
Bao, L., Liu, X., Xu, Z., and Fang, B. (2018). Autoconfig: automatic configuration tuning for distributed message systems. pages 29–40.
Chang, X., Lin, S.-B., and Zhou, D.-X. (2017). Distributed semi-supervised learning with kernel ridge regression. The Journal of Machine Learning Research, 18(1):1493–1514.
Chen, T. Y., Leung, H., and Mak, I. K. (2005). Adaptive random testing. In Maher, M. J., editor, Advances in Computer Science - ASIAN 2004. Higher-Level Decision Making, pages 320–329, Berlin, Heidelberg. Springer Berlin Heidelberg.
Chen, T. Y., Merkel, R., Wong, P., and Eddy, G. (2004). Adaptive random testing through dynamic partitioning. In Fourth International Conference onQuality Software, 2004. QSIC 2004. Proceedings., pages 79–86. IEEE.
Guo, J., Czarnecki, K., Apel, S., Siegmund, N., and Wasowski, A. (2013). Variability-aware performance prediction: A statistical learning approach. In 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE), pages 301–311. IEEE.
Heradio, R., Fernandez-Amoros, D., Galindo, J. A., Benavides, D., and Batory, D. (2022). Uniform and scalable sampling of highly configurable systems. Empirical Softw. Engg., 27(2).
Jamshidi, P., Camara, J., Schmerl, B., Kastner, C., and Garlan, D. (2019). Machine learning meets quantitative planning: Enabling self-adaptation in autonomous robots. arXiv preprint arXiv:1903.03920.
Johansen, M. F., Haugen, Ø., and Fleurey, F. (2012). An algorithm for generating t-wise covering arrays from large feature models. In SPLC’12, pages 46–55.
Kaltenecker, C., Grebhahn, A., Siegmund, N., Guo, J., and Apel, S. (2019). Distance-based sampling of software configuration spaces. In Proceedings of the International Conference on Software Engineering (ICSE).
Marijan, D., Gotlieb, A., Sen, S., and Hervieu, A. (2013). Practical pairwise testing for software product lines. In Proceedings of the 17th International Software Product Line Conference, SPLC ’13, page 227–235, New York, NY, USA. Association for Computing Machinery.
Pereira, J. A., Acher, M., Martin, H., Jézéquel, J.-M., Botterweck, G., and Ventresque, A. (2021). Learning software configuration spaces: A systematic literature review. Journal of Systems and Software, 182:111044.
Rajan, M. (2022). An efficient ridge regression algorithm with parameter estimation for data analysis in machine learning. SN Computer Science, 3(2):171.
Teaff, J., Young, B., and Clements, P. (2019). Applying feature-based systems and software product line engineering in unclassified and classified environments. INCOSE International Symposium, 29:269–283.
Vouk, B., Guid, M., and Robnik-Sikonja, M. (2023). Feature construction using explanations of individual predictions. Engineering Applications of Artificial Intelligence, 120:105823.
Acher, M., Martin, H., Lesoil, L., Blouin, A., Jézéquel, J.-M., Khelladi, D. E., Barais, O., and Pereira, J. A. (2022). Feature subset selection for learning huge configuration spaces: the case of linux kernel size. In Proceedings of the 26th ACM International Systems and Software Product Line Conference-Volume A, pages 85–96.
Acher, M., Martin, H., Pereira, J. A., Blouin, A., Jézéquel, J.-M., Khelladi, D. E., Lesoil, L., and Barais, O. (2019). Learning very large configuration spaces: What matters for linux kernel sizes. PhD thesis, Inria Rennes-Bretagne Atlantique.
Alves Pereira, J., Acher, M., Martin, H., and Jézéquel, J.-M. (2020). Sampling effect on performance prediction of configurable systems: A case study. page 277–288, New York, NY, USA. Association for Computing Machinery.
Amand, B., Cordy, M., Heymans, P., Acher, M., Temple, P., and Jézéquel, J.-M. (2019). Towards learning-aided configuration in 3d printing: Feasibility study and application to defect prediction. In Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems, page 7. ACM.
Bao, L., Liu, X., Xu, Z., and Fang, B. (2018). Autoconfig: automatic configuration tuning for distributed message systems. pages 29–40.
Chang, X., Lin, S.-B., and Zhou, D.-X. (2017). Distributed semi-supervised learning with kernel ridge regression. The Journal of Machine Learning Research, 18(1):1493–1514.
Chen, T. Y., Leung, H., and Mak, I. K. (2005). Adaptive random testing. In Maher, M. J., editor, Advances in Computer Science - ASIAN 2004. Higher-Level Decision Making, pages 320–329, Berlin, Heidelberg. Springer Berlin Heidelberg.
Chen, T. Y., Merkel, R., Wong, P., and Eddy, G. (2004). Adaptive random testing through dynamic partitioning. In Fourth International Conference onQuality Software, 2004. QSIC 2004. Proceedings., pages 79–86. IEEE.
Guo, J., Czarnecki, K., Apel, S., Siegmund, N., and Wasowski, A. (2013). Variability-aware performance prediction: A statistical learning approach. In 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE), pages 301–311. IEEE.
Heradio, R., Fernandez-Amoros, D., Galindo, J. A., Benavides, D., and Batory, D. (2022). Uniform and scalable sampling of highly configurable systems. Empirical Softw. Engg., 27(2).
Jamshidi, P., Camara, J., Schmerl, B., Kastner, C., and Garlan, D. (2019). Machine learning meets quantitative planning: Enabling self-adaptation in autonomous robots. arXiv preprint arXiv:1903.03920.
Johansen, M. F., Haugen, Ø., and Fleurey, F. (2012). An algorithm for generating t-wise covering arrays from large feature models. In SPLC’12, pages 46–55.
Kaltenecker, C., Grebhahn, A., Siegmund, N., Guo, J., and Apel, S. (2019). Distance-based sampling of software configuration spaces. In Proceedings of the International Conference on Software Engineering (ICSE).
Marijan, D., Gotlieb, A., Sen, S., and Hervieu, A. (2013). Practical pairwise testing for software product lines. In Proceedings of the 17th International Software Product Line Conference, SPLC ’13, page 227–235, New York, NY, USA. Association for Computing Machinery.
Pereira, J. A., Acher, M., Martin, H., Jézéquel, J.-M., Botterweck, G., and Ventresque, A. (2021). Learning software configuration spaces: A systematic literature review. Journal of Systems and Software, 182:111044.
Rajan, M. (2022). An efficient ridge regression algorithm with parameter estimation for data analysis in machine learning. SN Computer Science, 3(2):171.
Teaff, J., Young, B., and Clements, P. (2019). Applying feature-based systems and software product line engineering in unclassified and classified environments. INCOSE International Symposium, 29:269–283.
Vouk, B., Guid, M., and Robnik-Sikonja, M. (2023). Feature construction using explanations of individual predictions. Engineering Applications of Artificial Intelligence, 120:105823.
Publicado
20/05/2024
Como Citar
RODRIGUES, João Marcello Bessa; PEREIRA, Juliana Alves.
Effect of Feature Subset Selection on Samplings for Performance Prediction of Configurable Systems. In: CONCURSO DE TRABALHOS DE CONCLUSÃO DE CURSO EM SISTEMAS DE INFORMAÇÃO - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 20. , 2024, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 164-173.
DOI: https://doi.org/10.5753/sbsi_estendido.2024.238518.
