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Automatic Construction of Conceptual Models to Support Early Stages of Software Development - A Semantic Object Model Approach
[Thesis]. Manchester, UK: The University of Manchester; 2015.
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Abstract
The earliest stage of software development almost always involves converting requirements descriptions written in natural language (NLRs) into initial conceptual models, represented by some formal notation. This stage is time-consuming and demanding, as initial models are often constructed manually, requiring human modellers to have appropriate modelling knowledge and skills. Furthermore, this stage is critical, as errors made in initial models are costly to correct if left undetected until the later stages. Consequently, the need for automated tool support is desirable at this stage. There are many approaches that support the modelling process in the early stages of software development. The majority of approaches employ linguistic-driven analysis to extract essential information from input NLRs in order to create different types of conceptual models. However, the main difficulty to overcome is the ambiguous and incomplete nature of NLRs. Semantic-driven approaches have the potential to address the difficulties of NLRs, however, the current state of the art methods have not been designed to address the incomplete nature of NLRs.This thesis presents a semantic-driven automatic model construction approach which addresses the limitations of current semantic-driven NLR transformation approaches. Central to this approach is a set of primitive conceptual patterns called Semantic Object Models (SOMs), which superimpose a layer of semantics and structure on top of NLRs. These patterns serve as intermediate models to bridge the gap between NLRs and their initial conceptual models. The proposed approach first translates a given NLR into a set of individual SOM instances (SOMi) and then composes them into a knowledge representation network called Semantic Object Network (SON). The proposed approach is embodied in a software tool called TRAM. The validation results show that the proposed semantic-driven approach aids users in creating improved conceptual models. Moreover, practical evaluation of TRAM indicates that the proposed approach performs better than its peers and has the potential for use in real world software development.