http://dl.gi.de/handle/20.500.12116/40585 Tue, 21 Jul 2026 14:24:29 GMT 2026-07-21T14:24:29Z http://dl.gi.de/handle/20.500.12116/40613 RadarGun: Toward a Performance Testing Framework Henning, Sören; Wulf, Christian; Hasselbring, Wilhelm We present requirements on a performance testing framework to distinguish it from a functional testing framework and a benchmarking framework. Based on these requirements, we propose such a performance testing framework for Java, called RadarGun. RadarGun can be included into a continuous integration server, such as Jenkins, so that performance tests are executed automatically during the build process. We conducted a feasibility evaluation of this approach by applying it to the continuous integration infrastructure of the Pipe-and-Filter framework TeeTime. Sun, 01 Jan 2017 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40613 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/40612 Refactoring Kieker’s I/O Infrastructure to Improve Scalability and Extensibility Knoche, Holger Kieker supports several technologies for transferring monitoring records, including highly scalable messaging solutions. However, Kieker’s current I/O infrastructure is primarily built for point-to-point connections, making it difficult to leverage the scalability of these solutions. In this paper, we report on how we refactored Kieker’s I/O infrastructure to make better use of scalable messaging, improving extensibility along the way. Sun, 01 Jan 2017 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40612 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/40609 Providing Model-Extraction-as-a-Service for Architectural Performance Models Walter, Jürgen; Eismann, Simon; Reed, Nikolai; Kounev,Samuel Architectural performance models can be leveraged to explore performance properties of software systems during design-time and run-time. We see a reluctance from industry to adopt model-based analysis approaches due to the required expertise and modeling effort. Building models from scratch in an editor does not scale for medium and large scale systems in an industrial context. Existing open-source performance model extraction approaches imply significant initial efforts which might be challenging for layman users. To simplify usage, we provide the extraction of architectural performance models based on application monitoring traces as a web service. Model-Extraction-as-a-Service (MEaaS) solves the usability problem and lowers the initial effort of applying model-based analysis approaches. Sun, 01 Jan 2017 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40609 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/40611 The Raspberry Pi: A Platform for Replicable Performance Benchmarks? Knoche, Holger; Eichelberger, Holger Replicating results of performance benchmarks can be difficult. A common problem is that researchers often do not have access to identical hardware and software setups. Modern single-board computers like the Raspberry Pi are standardized, cheap, and powerful enough to run many benchmarks, although probably not at the same performance level as desktop or server hardware. In this paper, we use the MooBench micro-benchmark to investigate to what extent Raspberry Pi is suited as a platform for replicable performance benchmarks. We report on our approach to set up and run the experiments as well as the experience that we made. Sun, 01 Jan 2017 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40611 2017-01-01T00:00:00Z