A DSL-Based Framework for Performance Assessment

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Abstract

Performance assessment is an essential verification practice in both research and industry for software quality assurance. Experiment setups for performance assessment tend to be complex. A typical experiment needs to be run for a variety of involved hardware, software versions, system settings and input parameters. Typical approaches for performance assessment are based on scripts. They do not document all variants explicitly, which makes it hard to analyze and reproduce experiment results correctly. In general they tend to be monolithic which makes it hard to extend experiment setups systematically and to reuse features such as result storage and analysis consistently across experiments. In this paper, we present a generic approach and a DSL-based framework for performance assessment. The DSL helps the user to set and organize the variants in an experiment setup explicitly. The Runtime module in our framework executes experiments after which results are stored together with the corresponding setups in a database. Database queries provide easy access to the results of previous experiments and the correct analysis of experiment results in context of the experiment setup. Furthermore, we describe operations for common problems in performance assessment such as outlier detection. At Oracle, we successfully instantiate the framework and use it to nightly assess the performance of PGX [6, 12], a toolkit for parallel graph analytics.

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APA

Maazouz, H. E., Wachsmuth, G., Sevenich, M., Chiadmi, D., Hong, S., & Chafi, H. (2020). A DSL-Based Framework for Performance Assessment. In Learning and Analytics in Intelligent Systems (Vol. 7, pp. 260–270). Springer Nature. https://doi.org/10.1007/978-3-030-36778-7_28

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