Study of the performance of the negative selection algorithm applied to the detection of non-deterministic builds
Abstract
Builds, which are automated software deliveries and common practice in agile development environments, aims to compile, package, test, and deliver different versions of a system - which can result in success or failure, the latter is investigated manually. Through the bio-inspired Negative Selection Algorithm, those failures can be identified automatically, with good precision given a number of detectors. However, the time for this automation is long. This article aims to present the studies of the critical points found in this algorithm, the current solution reduces the total sequential execution time from 119.13s to 30.44 using 48 threads.
Keywords:
Parallel and Distributed Algorithms, High-Performance Applications
References
Cao, Q., Wen, R., and Mcintosh, S. (2017). Forecasting the duration of incremental build jobs. 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME).
de Castro, L. N. (2002). Artificial immune systems: a new computational intelligence approach. Springer.
de Lima Costa, J. C. (2020). Uma heurística de rotulação de builds com resultado não determinístico. Master’s thesis, Programa de Pós-Graduação em Engenharia Elétrica e Computação - Universidade Presbiteriana Mackenzie.
de Lima Costa, J. C., de Castro, L. N., and de Paula Bianchini, C. (2019). Sensitivity analysis of the negative selection algorithm applied to anomalies identification in builds. In 2019 XLV Latin American Computing Conference (CLEI), pages 1–6.
Ji, Z. and Dasgupta, D. (2009). V-detector: An efficient negative selection algorithm with “probably adequate” detector coverage. Information Sciences, 179(10):1390–1406.
Labuschagne, A., Inozemtseva, L., and Holmes, R. (2017). Measuring the cost of regression testing in practice: a study of java projects using continuous integration. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pages 821–830.
Pas, R. v. d., Stotzer, E., and Terboven, C. (2017). Using OpenMP - the next step: affinity, accelerators, tasking, and SIMD. the MIT Press.
de Castro, L. N. (2002). Artificial immune systems: a new computational intelligence approach. Springer.
de Lima Costa, J. C. (2020). Uma heurística de rotulação de builds com resultado não determinístico. Master’s thesis, Programa de Pós-Graduação em Engenharia Elétrica e Computação - Universidade Presbiteriana Mackenzie.
de Lima Costa, J. C., de Castro, L. N., and de Paula Bianchini, C. (2019). Sensitivity analysis of the negative selection algorithm applied to anomalies identification in builds. In 2019 XLV Latin American Computing Conference (CLEI), pages 1–6.
Ji, Z. and Dasgupta, D. (2009). V-detector: An efficient negative selection algorithm with “probably adequate” detector coverage. Information Sciences, 179(10):1390–1406.
Labuschagne, A., Inozemtseva, L., and Holmes, R. (2017). Measuring the cost of regression testing in practice: a study of java projects using continuous integration. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pages 821–830.
Pas, R. v. d., Stotzer, E., and Terboven, C. (2017). Using OpenMP - the next step: affinity, accelerators, tasking, and SIMD. the MIT Press.
Published
2021-05-06
How to Cite
MATRONE, Daniel; TSUNEKI, Roberto; PASQUALE, Rodrigo P.; BIANCHINI, Calebe P..
Study of the performance of the negative selection algorithm applied to the detection of non-deterministic builds. In: REGIONAL SCHOOL OF HIGH PERFORMANCE COMPUTING FROM SÃO PAULO (ERAD-SP), 12. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 53-56.
DOI: https://doi.org/10.5753/eradsp.2021.16704.
