Huerta-Guevara, OsvaldoOsvaldoHuerta-Guevara2020-11-042020-11-042019 the A2019http://hdl.handle.net/10197/11667Performance testing is a critical task to ensure an acceptable user experience with software systems, especially when there are high numbers of concurrent users. Selecting an appropriate test workload is a challenging and time-consuming process that relies heavily on the testers’ expertise. Not only are workloads application-dependent, but it is usually also unclear how large a workload must be to expose any performance issues that exist in an application. Previous research has proposed to dynamically adapt the test workloads in real-time, based on the application’s behavior. Workload adaptation claims to decrease the effort and expertise required to carry out performance testing, by reducing the need for trial-and-error test cycles (which occur when using static workloads). However, such approaches usually require testers to properly configure many parameters. This is cumbersome and hinders the usability and effectiveness of the approach, as a poor configuration, due to the use of inadequate test workloads, could lead to problems being overlooked. To address this problem, this thesis outlines and explains essential steps to conduct efficient performance testing using a dynamic workload adaptation approach, and examines the different factors influencing its performance. This research conducts a comprehensive evaluation of one of such approach to derive insights for practitioners w.r.t. how to fine-tune the process in order to obtain better outcomes based on different scenarios, as well as discuss the effects of varying its configuration, and how this can affect the results obtained. Furthermore, a novel tool was designed to improve the current implementation for dynamic workload adaptation. This tool is built on top of JMeter and aims to help advance research and practice in performance testing, using dynamic workload adaptation.enSoftware engineeringPerformance testingPerformance bugWorkloadMaster Thesis2020-09-11https://creativecommons.org/licenses/by-nc-nd/3.0/ie/