A Stream X-Machine tool for modelling and generating test cases for chronic diseases based on state-counting approach

Phung, Khoa; Jayatilake, Dilshan; Ogunshile, Emmanuel; Aydin, M.

Abstract

In the biomedical domain, diagrammatical models have been extensively used to describe and understand the behaviour of biological organisms (biological agents) for decades. Although these models are simple and comprehensive, they can only offer a static picture of the corresponding biological systems with limited scalability. As a result, there is an increasing demand to integrate formalism into more dynamic forms that can be more scalable and can capture complex time-dependent processes. Stream X-Machine (SXM) is such a powerful formal method with a memory (data) structure and function-labelled transitions. One of the main strengths of the SXM is its associated testing strategy which ensures that, under well-defined conditions, all functional inconsistencies between the system under test and the model are revealed. In this paper, we adopt the concept of SXM to develop a tool known as T-SXM, which has the capabilities of modelling real world problems and generating test cases automatically based on the state-counting approach. The Type II diabetes case study has been used to demonstrate the abilities of the proposed tool.

• Journal Article Type: Article
• Acceptance Date: Aug 10, 2021
• Online Publication Date: Dec 28, 2021
• Publication Date: 2021-12
• Deposit Date: Aug 12, 2021
• Publicly Available Date: Dec 29, 2022
• Journal: Programming and Computer Software
• Print ISSN: 0361-7688
• Electronic ISSN: 1608-3261
• Publisher: MAIK Nauka/Interperiodica
• Peer Reviewed: Peer Reviewed
• Volume: 47
• Issue: 8
• Pages: 765-777
• Series ISSN: Programming and Computer Software, Springer ISSN 0361-7688, IF 0.936, Q4 (Scopus: Q3)
• DOI: https://doi.org/10.1134/S0361768821080211
• Public URL: https://uwe-repository.worktribe.com/output/7618324
• Publisher URL: https://www.springer.com/journal/11086