E. Altman, M. Arnold, S. Fink, and N. Mitchell, Performance analysis of idle programs, ACM SIGPLAN Notices, vol.45, issue.10, 2010.

V. Angelopoulos, T. Parsons, J. Murphy, and P. O'sullivan, GcLite: An Expert Tool for Analyzing Garbage Collection Behavior, 2012 IEEE 36th Annual Computer Software and Applications Conference Workshops, pp.493-502, 2012.

V. Ayala-rivera, M. Kaczmarski, J. Murphy, A. Darisa, and A. O. Portillo-dominguez, One Size Does Not Fit All, ICPE '18, pp.211-222, 2018.

M. W. Aziz and S. A. Shah, Test-data generation for testing parallel real-time systems, IFIP -ICTSS '15, pp.211-223, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01470169

J. Basak, K. Wadhwani, and K. Voruganti, Storage Workload Identification, ACM Transactions on Storage, vol.12, issue.3, pp.1-30, 2016.

D. A. Bourne, A. A. Chung, and D. L. Price, Capturing trace information using annotated trace output, US Patent 9, vol.355, p.2, 2016.

M. Bures and . Miroslav, Metrics for automated testability of web applications, CompSysTech '15, pp.83-89, 2015.

A. De-camargo, I. Salvadori, R. D. Mello, and F. Siqueira, An architecture to automate performance tests on microservices, iiWAS '16, pp.422-429, 2016.

C. D. Carothers, J. S. Meredith, M. P. Blanco, J. S. Vetter, M. Mubarak et al., Durango: Scalable Synthetic Workload Generation for Extreme-Scale Application Performance Modeling and Simulation, SIGSIM-PADS '17, pp.97-108, 2017.

T. H. Chen, M. D. Syer, W. Shang, Z. M. Jiang, A. E. Hassan et al., Analytics-Driven Load Testing: An Industrial Experience Report on Load Testing of Large-Scale Systems. In: ICSE-SEIP, 2017.

M. Conley, A. Vahdat, and G. Porter, Achieving cost-efficient, data-intensive computing in the cloud, SoCC'15, pp.302-314, 2015.

M. Curiel and A. Pont, Workload generators for web-based systems: Characteristics, current status, and challenges, IEEE Communications Surveys Tutorials, vol.20, issue.2, pp.1526-1546, 2018.

I. Drave, S. Hillemacher, T. Greifenberg, S. Kriebel, E. Kusmenko et al., Smardt modeling for automotive software testing. Software: Practice and Experience, vol.49, pp.301-328, 2019.

W. Dulz, A Versatile Tool Environment to Perform Model-based Testing of Web Applications and Multilingual Websites, pp.45-56, 2018.

V. Ferme and C. Pautasso, A Declarative Approach for Performance Tests Execution in Continuous Software Development Environments, ICPE '18, pp.261-272, 2018.

A. Furda, C. Fidge, A. Barros, and O. Zimmermann, Reengineering data-centric information systems for the cloud-a method and architectural patterns promoting multitenancy, Software Architecture for Big Data and the Cloud, 2017.

M. Grechanik, Q. Luo, D. Poshyvanyk, and A. Porter, Enhancing Rules For Cloud Resource Provisioning Via Learned Software Performance Models, ICPE '16, 2016.

M. M. Henein, D. M. Shawky, and S. K. Abd-el-hafiz, Clustering-based Undersampling for Software Defect Prediction, 2018.

I. Hooda and R. S. Chhillar, Software test process, testing types and techniques, International Journal of Computer Applications, vol.111, issue.13, 2015.

W. Huang and J. Peleska, Safety-complete test suites, IFIP -ICTSS '17, pp.145-161, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01678989

Z. M. Jiang and Z. Ming, Automated analysis of load testing results, ISSTA '10, p.143, 2010.

M. Kaczmarski, P. Perry, J. Murphy, and A. O. Portillo-dominguez, In-test adaptation of workload in enterprise application performance testing, p.17, 2017.

M. Krichen, A. J. Maâlej, and M. Lahami, A model-based approach to combine conformance and load tests: an ehealth case study, International Journal of Critical Computer-Based Systems, vol.8, issue.3-4, pp.282-310, 2018.

Q. Luo, D. Poshyvanyk, A. Nair, and M. Grechanik, FOREPOST: a tool for detecting performance problems with feedback-driven learning software testing, 38th ICSE-C, pp.593-596, 2016.

A. J. Maâlej and M. Krichen, A model based approach to combine load and functional tests for service oriented architectures, VECoS, pp.123-140, 2016.

M. Markthaler, S. Kriebel, K. S. Salman, T. Greifenberg, S. Hillemacher et al., Improving model-based testing in automotive software engineering, ICSE-SEIP, pp.172-180, 2018.

J. D. Meier, C. Farre, P. Bansode, S. Barber, and D. Rea, Performance testing guidance for web applications: Patterns & Practices. Microsoft, 2007.

A. O. Portillo-dominguez and V. Ayala-rivera, Improving the testing of clustered systems through the effective usage of java benchmarks, 2017.

A. O. Portillo-dominguez, P. Perry, D. Magoni, and J. Murphy, PHOEBE: an automation framework for the effective usage of diagnosis tools in the performance testing of clustered systems, Software: Practice and Experience, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01527908

A. O. Portillo-dominguez, M. Wang, J. Murphy, and D. Magoni, Automated WAIT for cloud-based application testing, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01009432

A. O. Portillo-domínguez, J. Murphy, and P. O'sullivan, Leverage of extended information to enhance the performance of JEE systems, IT&T, vol.2012, 2012.

R. Ramakrishnan, V. Shrawan, and P. Singh, Setting realistic think times in performance testing: A practitioner's approach, ISEC'17, pp.157-164, 2017.

D. G. Reichelt and S. Kühne, Better Early Than Never, ICPE '18, 2018.

D. G. Reichelt and S. Kühne, How to Detect Performance Changes in Software History, ICPE '18, 2018.

A. B. Sánchez, P. Delgado-pérez, S. Segura, and I. Medina-bulo, Performance mutation testing: Hypothesis and open questions, Information and Software Technology, vol.103, pp.159-161, 2018.

S. Segura, J. Troya, A. Duran, and A. Ruiz-cortes, Performance Metamorphic Testing: Motivation and Challenges, 2017.

M. Shams, D. Krishnamurthy, and B. Far, A model-based approach for testing the performance of web applications, SOQUA '06, p.54, 2006.

W. Spear, S. Shende, A. Malony, R. Portillo, P. J. Teller et al., Making Performance Analysis and Tuning Part of the Software Development Cycle, DoD High Performance Computing Modernization Program Users Group Conference, 2009.

W. Tang, Y. Fu, L. Cherkasova, and A. Vahdat, Medisyn: A synthetic streaming media service workload generator, NOSSDAV '03, p.12, 2003.

J. Troya, S. Segura, and A. Ruiz-cortés, Automated inference of likely metamorphic relations for model transformations, Journal of Systems and Software, vol.136, 2018.

H. Wu, A. N. Tantawi, and T. Yu, A Self-Optimizing Workload Management Solution for Cloud Applications, ICWS 2013, pp.483-490, 2013.