Framework for preparing subject data in testing modules of scientific applications

Abstract

The paper addresses the relevant problem of data preparation for testing modules of scientific applications. Such testing requires the multiple executions of modules with different parameters for various scenarios of solving problems in applications. Often, data sources for parameters used for problem-solving are subject data (experimental results, reports, statistical forms and other information resources) created earlier as a result of functioning various objects of a subject domain. Usually, such data are heterogeneous and weakly structured. The developer of scientific applications has to make additional efforts in extracting, cleaning, integrating, and formatting data in order to achieve the correctness and efficiency of their use in applications. The aim of the study is the development of a framework for automating the description of semi-structured data and their transformation into target structures used by scientific applications. We proposed a conceptual model that allows us to represent knowledge about the structure of the source data, determine their relations with the target structures and set the rules for data transformation. Additionally, we developed a framework prototype. It is integrated into the technological scheme of continuous integration for modules of scientific applications (distributed applied software packages) that are developed with the help of Orlando Tools. The effectiveness of this prototype functioning is confirmed by the results of experimental analysis.