Systems engineering in the large based graph database systems

OData support
Supervisor:
Dr. Ráth István Zoltán
Department of Measurement and Information Systems

Nowadays, we are developing more and more complex IT systems, in wich, it is a critical question to estimate costs and deadlines and to reach the desired quality. These are challenging tasks that can be easier with Application Life Management and Product Lifecycle Management tools. ALM and PLM tools cover the entire lifecycle from the idea, through the development, testing, deployment, support, and operation of a system.

In such a management environment, there are many data sources, such as requirements management tools, design and simulation programs, and code and database management systems. These tools cover only a part of the problem space, so a development typically involves the simultaneous and integrated use of many devices. In practice, however, integration is generally not complete, despite the fact that the connection between the information handled by the different tools is strong and important. Recognizing and exploiting such connections are critical for quality assurance and economical aspects.

The subject of the essay is the design and development of an integration platform that provides an effective, model-based solution for linking data accessible through open interfaces of various modern ALM/PLM systems. Data storing relies on a graph-based approach using the IncQuery system, which approach is suitable for the integrated management and efficient evaluation of information from different sources. The solution also provides a powerful tool to run graph pattern-based VQL (VIATRA Query Language) queries, which can be used to extract complex data interdependencies.

I introduce the system through a case study. During my work I implemented several data integration modules, which are designed to crawl the TeamWork Cloud model storage and version control system, the Github repository and issue tracker, the build information that can be found on the Jenkins server, and the SonarQube static source code analyses of a certain project, then process the accessible data, and evaluate the graph queries developed by myself. The evaluation of the case study extends to the efficiency analytics too, which were used to verify the practical usability of the method.

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