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2018
Doctoral Thesis
Title
Proactive support for conceptual interoperability analysis of software units
Abstract
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.
Thesis Note
Zugl.: Kaiserslautern, TU, Diss., 2017
Keyword(s)
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