PhD Theses in Experimental Software Engineering, Band 60
Hrsg.: Frank Bomarius, Peter Liggesmeyer, Dieter Rombach; Fraunhofer IESE, Kaiserslautern
2018, 282 S., num. illus. and tab., Softcover
Kaiserslautern, TU, Diss., 2017
Meaningful exchange of data or services with a software unit requires identifying and satisfying its conceptual constraints. Otherwise, unexpected conceptual mismatches lead to late projects and costly rework. However, for blackbox software providers, it is unguided and time-consuming task to share the conceptual constraints explicitly with third-party clients who also lack the guidance on detecting the conceptual mismatches. To cope with these challenges, we built a Conceptual Interoperability Constraints (COINs) model, which is the base for our Conceptual Interoperability Analysis (COINA) framework. COINA helps architects and analysts to identify the conceptual constraints and mismatches of software units effectively and efficiently. It comprises: (1) Proactive, semi-automatic, in-house preparation for interoperable units that helps providers to share the conceptual constraints with the least effort. (2) A systematic, algorithm-based method for mapping conceptual constraints of systems to detect their mismatches. A multi-run controlled experiment confirmed our hypotheses that our approach significantly increases the effectiveness and efficiency in detecting conceptual mismatches.
Fraunhofer IESE, Unified Modeling Language (UML), systems analysis & design, scientific equipment, experiments & techniques, machine learning, software architecture, software interoperability, conceptual constraint and mismatch, empirical software engineering, machine learning, interoperability analysis, software engineer, software architect, integration analysts,
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