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In-Test Adaptation of Workload in Enterprise Application Performance Testing.

, , , и . ICPE Companion, стр. 69-72. ACM, (2017)

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Mohamed A Omar

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In-Test Adaptation of Workload in Enterprise Application Performance Testing., , , и . ICPE Companion, стр. 69-72. ACM, (2017)Analysis of Machine Learning Techniques for Anomaly Detection in the Internet of Things., , , , и . LA-CCI, стр. 1-6. IEEE, (2018)GDPR compliance via software evolution: Weaving security controls in software design., , и . J. Syst. Softw., (2024)Choosing Machine Learning Algorithms for Anomaly Detection in Smart Building IoT Scenarios., , , и . WF-IoT, стр. 491-495. IEEE, (2019)One Size Does Not Fit All: In-Test Workload Adaptation for Performance Testing of Enterprise Applications., , , , и . ICPE, стр. 211-222. ACM, (2018)COCOA: A Synthetic Data Generator for Testing Anonymization Techniques., , , и . PSD, том 9867 из Lecture Notes in Computer Science, стр. 163-177. Springer, (2016)DYNAMOJM: A JMeter Tool for Performance Testing Using Dynamic Workload Adaptation., , , и . ICTSS, том 11812 из Lecture Notes in Computer Science, стр. 234-241. Springer, (2019)Towards an Efficient Benchmark Generation Engine for Garbage Collection.. ICPE Companion, стр. 9-12. ACM, (2018)A Requirements-Based Approach for the Evaluation of Emulated IoT Systems., и . RESACS@RE, стр. 16-19. IEEE, (2018)Towards an Efficient Performance Testing Through Dynamic Workload Adaptation., , , и . ICTSS, том 11812 из Lecture Notes in Computer Science, стр. 215-233. Springer, (2019)