An algorithm for implementing a minimal stream X-Machine model to test the correctness of a system

Ogunshile, Emmanuel; Phung, Khoa

Abstract

The rapid change of requirements has made software more complex and harder to maintain. Software testing tools play an important role in the Software Development Life Cycle. However, many technology companies have employed fast paced development of software to meet the demands of their markets. Early released software tends to contain serious bugs and errors because they have not been tested properly during the testing process. Many current available testing tools are capable of detecting these faults; however, some of which are either outdated or cannot catch up with the constantly changing demands. Also, these testing tools concentrate more on generating efficient test sets while ignoring their effectiveness. This paper introduces an algorithm called T-SXM which is developed using the Java programming language based on the stream X-Machines theory which is an intuitive and powerful technique for modelling real world problems formally. The T-SXM algorithm is designed to provide developers with a tool to test the correctness of their implemented systems which can potentially resolve the problem of effectiveness that the other testing tools are facing.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: CONISOFT 2020 : IEEE 8th International Conference on Software Engineering Research and Innovation
• Start Date: Nov 4, 2020
• End Date: Nov 6, 2020
• Acceptance Date: Jul 10, 2020
• Online Publication Date: Dec 31, 2020
• Deposit Date: Jul 15, 2020
• Publicly Available Date: Feb 1, 2021
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Book Title: 2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT)
• ISBN: 9781728184517
• DOI: https://doi.org/10.1109/CONISOFT50191.2020.00023
• Keywords: component; T-SXM; testing strategy; transform; model; specification; correctness; effectiveness
• Public URL: https://uwe-repository.worktribe.com/output/6250491
• Publisher URL: http://conisoft.org/2020/