• Oluwatoni Olatunji

Student thesis: Phd


Requirements Engineering (RE) is the process of discovering and defining user requirements for developing software systems. The early phase of this process is concerned with eliciting and analysing requirements. Modelling, the activity of constructing abstract descriptions that are amenable to communication between different stake-holders plays a critical role in requirements elicitation and analysis. However, current modelling tools are based on formal notations such as UML Diagrams and i* Diagram that do not support the use of hand-drawn diagrams or a mix of hand-drawn and computer drawn diagram to draw initial requirements models and subsequently transform the drawn models into target software models. The research presented in this thesis aims to address this problem. It aims to achieve two related objectives: 1) to develop a sketch tool, iSketch, that would enable users to draw use case diagram using either hand-drawn diagram or a mix of hand-drawn and computer drawn diagram. and 2) to support the transformation of the drawn use case diagram into initial software models represented by UML Class Diagram and UML Sequence Diagram. Central to these research objectives are the development of novel sketch recognition and model transformation techniques for iSketch. To support sketch recognition, we have developed a deep learning technique that uses colour inversion to classify and improve the recognition rate of iSketch models. To support model transformation, we have developed a semantic modelling approach that works by first translating iSketch models into intermediate Agent-Oriented Models and finally into initial software models. iSketch was evaluated in two ways. First, validation of iSketch through 2 experiments to measure the performance of iSketch in sketch recognition and model transformation using stroke labelling, and f-score metrics, respectively. In sketch recognition, iSketch achieved a recognition accuracy of 89.91% and 97.29% without and with colour inversion respectively when tested on iSketch dataset. In model transformation, iSketch achieved an f-score of 91.22% and 60.88% in generating UML Sequence and Class Diagrams respectively from iSketch models. Second, iSketch was compared with 15 related approaches. The result showed that only iSketch supports an automatic generation of initial software models from hand-drawn requirements models.
Date of Award1 Aug 2021
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorLiping Zhao (Supervisor) & Kung-Kiu Lau (Supervisor)


  • Sketch Recognition
  • Convolutional Neural Network
  • Sketch
  • Requirements Engineering
  • Requirements Elicitation
  • Model Transformation
  • Requirements Modelling
  • Domain Specific Language

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