http://dl.gi.de/handle/20.500.12116/40508 Tue, 21 Jul 2026 14:24:28 GMT 2026-07-21T14:24:28Z http://dl.gi.de/handle/20.500.12116/40541 Lessons Learned from Analyzing Requirements Traceability using a Graph Database Goman, Maksim; Rath, Michael; Mäder, Patrick Established traceability among development artifacts allows to apply structured analysis in order to answer questions posed by stakeholders. Typically, the artifacts and their links are stored in relational databases. However, answering trace related questions involves finding paths and patterns in the artifact graph - a difficult task to perform using generic query languages. Mapping the artifact and link data onto graph databases and utilizing specialized query languages may overcome this limitation. In this paper, this mapping from a relational traceability dataset to a graph database is demonstrated. Afterwards, the advantages and disadvantages of the approach are investigated by calculating three trace metrics, heavily relying on graph patterns, using a graph query language. Overall, utilizing a graph database proved to simplify traceability analysis. Mon, 01 Jan 2018 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40541 2018-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/40542 Gleitender Übergang vom manuellen zum automatisierten Test eingebetteter Software Bürsner, Simone; Hess, Anne; Queins, Stefan; Schmid, Klaus; Seyff, Norbert Mon, 01 Jan 2018 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40542 2018-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/40539 Preprocessing Texts in Issue Tracking Systems to improve IR Techniques for Trace Creation Todorova Tomova, Mihaela; Mäder, Patrick Multiple studies showed the usefulness of requirements traceability in developing software and systems. Still, a major challenge is to establish the required trace links among development artifacts. Often, information retrieval (IR) techniques combined with text similarity measures are used for this task. Applying these ideas to requirements texts found in issue tracking systems (ITS) of open source systems is difficult, because often these texts are structured and not only contain natural language. Thus, preprocessing of the textual information is required to extract the different kinds of text. In this paper, the authors study the structure of issue descriptions found in open source systems and identify several categories of text found therein, such as source code and stack traces. These text categories allow a more precise application of similarity analysis in order to create traces by comparing textual information of the same kind, i. e. source code with source code and natural language with natural language. Mon, 01 Jan 2018 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40539 2018-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/40540 Traceability enabled by Metamodel Integration Meier, Johannes; Winter, Andreas Traceability supports software development by connecting separated software artifacts explicitly with each other using traceability links. While intra traceability within single artifacts is solvable by a single metamodel, for inter traceability between different separated artifacts their metamodels have to be related to each other. Therefore, this paper presents an approach to realize inter traceability by integrating the metamodels of the artifacts into one metamodel together with their traceability information. The approach is demonstrated in a small software project using requirements, class diagrams, and source code. Mon, 01 Jan 2018 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40540 2018-01-01T00:00:00Z